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At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it's work worth doing. If you're driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. Where AI Meets Medicine: Build the Future of Drug Discovery in the Heart of Silicon Valley! Making medicine that's never been made means doing what's never been done. If you're an engineer, scientist, or builder who thrives on problems no one has solved before, this is your invitation; we want you on the team. We are ready to challenge the status quo and push medicine forward, all in the name of health. Are you up for the challenge? If so, join us! About The Lilly And NVIDIA Partnership Lilly and NVIDIA are launching a new AI co-innovation lab in the heart of Silicon Valley — an up-to-$1 billion, multi-year commitment to solve drug discovery's toughest challenges. The lab brings Lilly scientists, technologists, chemists and biologists together with NVIDIA engineers under one roof. Together, we are building purpose-built foundation and frontier AI models trained on Lilly data at scale, tightening the feedback loop between automated wet labs and computational dry labs, designing the next generation of medicines for millions of patients across the globe. What You'll Be Doing As an ML Ops Engineer, you build and operate the platforms that run the end-to-end machine learning lifecycle. You enable reliable model deployment, operation, monitoring, retraining, and reproducibility at scale. You optimize infrastructure and GPU resources to support research and discovery workloads. You will work closely with engineering and scientific teams to deliver production-ready AI capabilities. How You'll Succeed Lead the operational lifecycle of ML models, including deployment, monitoring, and ongoing reliability. Operate and optimize large-scale inference platforms that support scientific discovery and AI workloads. Ensure models can be deployed, scaled, monitored, and maintained in production environments. Test, refine, and improve model accuracy. Work with data scientists, business analysts and partners to integrate ML models into broader strategies. Automate the platform with infrastructure-as-code and CI/CD, and document it well enough that someone else can operate it. What You Should Bring Strong Python skills and experience working with machine learning frameworks such as PyTorch, JAX, or TensorFlow. Experience deploying, operating, and scaling production machine learning platforms, including model serving, monitoring, and large-scale inference workloads. Experience with MLOps platforms and tools like MLflow, Weights & Biases, KServe, or similar technologies. Proficiency with containerization, orchestration, and distributed compute environments (Docker, Kubernetes, Slurm, Ray). Experience operating large-scale AI platforms that deploy, host, and optimize machine learning models for production use, using technologies such as Triton, vLLM, or TensorRT-LLM. Experience with infrastructure automation and CI/CD practices using tools such as Terraform, Ansible, GitHub Actions, or related. Experience supporting cloud platforms (AWS, Azure, or GCP) and on-premises GPU infrastructure. Knowledge of observability and operational monitoring, including metrics, logging, tracing, and performance tuning. Ability to identify and address system, infrastructure, and model performance issues through automation and continuous improvement. Ability to collaborate effectively with research scientists, AI engineers, and infrastructure teams in a fast-paced environment. Your Basic Qualifications Bachelor's in Computer Science, Engineering, Statistics, Mathematics, or a related technical field 4+ years of experience in machine learning engineering, ML Ops, or platform engineering. Location & Work Flexibility This role is based at our Silicon Valley Hub. We offer a flexible hybrid work model, with three days onsite and two days working remotely each week , supporting both collaboration and work‑life balance. Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response. Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status. Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia (AMECA), Black Employees at Lilly (BE@Lilly), Chinese Culture Network (CCN), EnAble, Evolve, Lilly Indian Network (LIN), Organization of Latinx at Lilly (OLA), Pride (LGBTQ+ Allies), Veterans Leadership Network (VLN) and Women's Initiative for Leading at Lilly (WILL). Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is $147,000 - $268,400 Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly's compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees. #WeAreLilly

Job Posting Title: AI Engineer Hiring Department: Dell Medical School Position Open To: All Applicants Weekly Scheduled Hours: 40 FLSA Status: Exempt from FLSA Earliest Start Date: Immediately Position Duration: Expected to Continue Location: AUSTIN, TX Job Details: General Notes The AI/Machine Engineer II supports the development, deployment, and monitoring of machine learning models in production environments. Reporting to the Director of Data Intelligence and Decision Science, this career-development role focuses on applying and expanding proficiency in MLOps practices. The AI/Machine Engineer II uses practical knowledge of machine learning operations to help build scalable, secure, and reliable ML infrastructure in a healthcare setting. Responsibilities Machine Learning Deployment and Governance Develops and refines ML deployment pipelines and workflows to support production-grade machine learning lifecycle governance and management. Implements lifecycle governance standards for versioning and reproducibility. Optimizes workflows for scalability and reliability. Model Monitoring and Performance Implements and maintains monitoring systems to proactively assess model performance, reliability, and drift. Detects and reports model drift and anomalies. Generates dashboards and alerts for stakeholders. Cross-Functional Collaboration Collaborates with data scientists, software engineers, and DevOps teams to translate model requirements into operational solutions. Coordinates integration with APIs and application workflows. Participates in code reviews and design sessions. Automation of Retraining and Deployment Designs and automates retraining, validation, and deployment processes to ensure model accuracy and responsiveness. Implements checks before deployment. Schedules retraining jobs based on triggers. Data Governance and Security Applies healthcare data governance and security protocols to ML systems, ensuring compliance with regulatory standards. Implements access controls and encryption. Supports compliance audits and documentation. Cloud Infrastructure Optimization Configures and optimizes cloud infrastructure for ML workloads, balancing performance and cost-efficiency. Tunes resource allocation for cost and performance. Monitors infrastructure utilization and scaling needs. Documentation and Knowledge Sharing Contributes to technical documentation, process improvement, and knowledge sharing across teams. Maintains technical documentation for ML workflows. Shares best practices and lessons learned. Marginal Or Periodic Functions Assists with proof-of-concept evaluations for new MLOps tools. Participates in post-mortem reviews for ML pipeline incidents. Supports periodic updates to model documentation and compliance records. Adheres to internal controls and reporting structure. Performs related duties as required. Knowledge/Skills/Abilities Functional/Technical Skills Demonstrates proficiency in programming and scripting for ML workflows and automation. Applies knowledge of machine learning concepts, model lifecycle, and production deployment. Possesses a working knowledge of CI/CD pipelines, cloud infrastructure, and container orchestration. Problem Solving Troubleshoots and optimizes ML systems in dynamic, real-world environments. Exhibits strong analytical and problem-solving skills for identifying and resolving operational challenges. Initiates process improvements and adopts emerging MLOps practices. Adaptability and Continuous Learning Commits to continuous learning and professional development in machine learning operations. Adapts quickly to new tools and emerging best practices. Prototypes with unfamiliar libraries and seeks feedback. Process Management Organizes ML workflows for efficiency and reliability. Automates retraining and evaluation jobs. Standardizes packaging and environments for reproducibility. Dealing with Ambiguity Handles unclear requirements through testing and iteration. Communicates trade-offs clearly and revisits decisions as new data emerges. Designs small experiments to reduce uncertainty. Peer Relationships Communicates and collaborates effectively across technical and non-technical teams. Participates in reviews and stand-ups to share updates. Co-develops APIs and integration points. Required Qualifications Bachelor's degree in Computer Science, Data Science, Engineering, or a related field. Minimum 2 years of relevant experience. Relevant education and experience may be substituted as appropriate. Preferred Qualifications 3+ years of experience in MLOps, DevOps, or ML engineering. Experience in healthcare or regulated environments. Licenses, Registrations or Certifications Required: None Preferred: Cloud platform certifications (e.g., foundational or associate-level). Introductory certifications in machine learning or DevOps practices. Salary Range $89,946 + depending on qualifications Working Conditions Standard office equipment Repetitive use of a keyboard May be exposed to such occupational hazards as communicable diseases, blood borne pathogens, ionizing and non-ionizing radiation, hazardous medications and disoriented or combative patients, or others. Required Materials Resume/CV 3 work references with their contact information; at least one reference should be from a supervisor Letter of interest Important for applicants who are NOT current university employees or contingent workers: You will be prompted to submit your resume the first time you apply, then you will be provided an option to upload a new Resume for subsequent applications. Any additional Required Materials (letter of interest, references, etc.) will be uploaded in the Application Questions section; you will be able to multi-select additional files. Before submitting your online job application, ensure that ALL Required Materials have been uploaded. Once your job application has been submitted, you cannot make changes. Important for Current university employees and contingent workers: As a current university employee or contingent worker, you MUST apply within Workday by searching for Find UT Jobs. If you are a current University employee, log-in to Workday, navigate to your Worker Profile, click the Career link in the left hand navigation menu and then update the sections in your Professional Profile before you apply. This information will be pulled in to your application. The application is one page and you will be prompted to upload your resume. In addition, you must respond to the application questions presented to upload any additional Required Materials (letter of interest, references, etc.) that were noted above. Employment Eligibility: Regular staff who have been employed in their current position for the last six continuous months are eligible for openings being recruited for through University-Wide or Open Recruiting, to include both promotional opportunities and lateral transfers. Staff who are promotion/transfer eligible may apply for positions without supervisor approval. Retirement Plan Eligibility: The retirement plan for this position is Teacher Retirement System of Texas (TRS), subject to the position being at least 20 hours per week and at least 135 days in length. Background Checks: A criminal history background check will be required for finalist(s) under consideration for this position. Equal Opportunity Employer: The University of Texas at Austin, as an equal opportunity/affirmative action employer, complies with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, national origin, age, marital status, sex, sexual orientation, gender identity, gender expression, disability, religion, or veteran status in employment, educational programs and activities, and admissions. Pay Transparency: The University of Texas at Austin will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor's legal duty to furnish information. Employment Eligibility Verification: If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present acceptable and original documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment. Failure to do so will result in loss of employment at the university. E-Verify: The University of Texas at Austin use E-Verify to check the work authorization of all new hires effective May 2015. The university's company ID number for purposes of E-Verify is 854197. For more information about E-Verify, please see the following: E-Verify Poster (English and Spanish) [PDF] Right to Work Poster (English) [PDF] Right to Work Poster (Spanish) [PDF] Compliance: Employees may be required to report violations of law under Title IX and the Jeanne Clery Disclosure of Campus Security Policy and Crime Statistics Act (Clery Act). If this position is identified a Campus Security Authority (Clery Act), you will be notified and provided resources for reporting. Responsible employees under Title IX are defined and outlined in HOP-3031. The Clery Act requires all prospective employees be notified of the availability of the Annual Security and Fire Safety report. You may access the most recent report here or obtain a copy at University Compliance Services, 1616 Guadalupe Street, UTA 2.206, Austin, Texas 78701.

Atomic Machines is building the Matter Compiler™, the first in a new class of AI-native, all-digital manufacturing systems that build working machines directly from code. The Matter Compiler™ is aimed at micro-machines: motors and gears the size of a grain of sand, robots small enough to work inside the body, cooling built directly into the chips that run AI, and the many devices like them that have been waiting on a way to build them. Its first product is PrimeSwitch™, an electromechanical power relay the size of a button battery that carries 150 amps continuous current and opens in 50 microseconds, about a thousand times faster than a conventional contactor. Founded by Jeff Holden — serial entrepreneur, ex-Amazon SVP and Uber's first Chief Product Officer — the company has raised $250 million to date. Atomic Machines is based in the San Francisco Bay Area, with offices in Emeryville and Santa Clara. About The Role: We are seeking an MLOps Engineer to join our AI and Modeling & Simulation org within the Data Engineering and Analytics team. You will build and operate the infrastructure that takes AI and machine learning models from experimentation to reliable production - covering training, deployment, serving, monitoring, and continuous improvement. This is a DevOps-leaning MLOps role centered on the model feedback loop: connecting production signals and expert feedback back to training so models improve as the system operates. We are looking for senior-level candidates who can take meaningful ownership of production ML infrastructure. The scope and seniority of the role will be shaped by the candidate's experience, technical depth, and demonstrated impact. You will work closely with Data, AI, Process, Design, and Software engineers in a highly cross-functional environment. What You'll Do: Build and evolve the MLOps platform and CI/CD : Own the path from experiment to production, including experiment tracking, model registry, packaging, automated training and retraining, deployment, and safe rollout and rollback. Operate model serving infrastructure: Build reliable, scalable batch, streaming, and real-time inference for models and digital twins supporting design, process control, scheduling, and inspection. Build ML data and feature pipelines: Turn machine telemetry, process and knowledge graphs, images, time-series, agentic conversations, and other production data into contextualized, model-ready datasets and features. Maintain ML data infrastructure: Support feature-store capabilities and a lakehouse foundation using Apache Iceberg on S3, with strong data quality, lineage, versioning, and reproducibility. Close the model feedback loop: Build model observability and human-in-the-loop systems that capture production signals and expert corrections, version them as ground truth, and feed them into evaluation and retraining workflows. Create paved roads for ML development: Develop standardized tooling and workflows that enable Data and AI engineers to move quickly while maintaining production reliability and reproducibility. Drive technical ownership: Identify infrastructure, reliability, and scalability challenges and drive solutions from design through production. More senior candidates will have opportunities to shape architecture, technical direction, and engineering practices across the ML platform. Collaborate across disciplines: Work with Process, Chemical, Materials, Simulation, Software, Data, and AI engineers to define deployment, serving, and data-collection requirements. What You'll Need: 5+ years of relevant industry experience building production software, infrastructure, data, or machine learning systems. We value demonstrated technical depth, ownership, and impact over a specific number of years. Proven experience building and operating machine learning systems in production, with a strong MLOps/DevOps orientation. Strong DevOps fundamentals, including CI/CD , containers, Kubernetes , cloud infrastructure, and infrastructure-as-code. Proficiency in Python and SQL. Hands-on experience with MLflow or similar tooling for experiment tracking, model registry, and model lifecycle management. Experience with S3, lakehouse technologies such as Apache Iceberg, and workflow orchestration tools such as Airflow or Dagster . Experience building pipelines for multimodal ML data, including images, time-series, structured, and semi-structured data. Familiarity with manufacturing systems, sensors, process automation, or other physical-world data systems. Strong problem-solving skills, attention to data quality and reliability, and clear technical communication. Bachelor's or Master's degree in Computer Science, Data Engineering, Data Science, or a related STEM field, or equivalent practical experience. This role is open across multiple levels, from early in career though Staff (L4 through L6). We'll determine the appropriate level and compensation based on your experience, skills, and the scope of the role through the interview process. Bonus Points For: Experience with feature stores, human-in-the-loop systems, active learning, or data-labeling infrastructure. Robotics or robotic automation experience, including sensors, vision systems, or robotics data. Experience operating ML systems in manufacturing or other physical-world environments. Experience building internal tools for expert feedback, labeling, model evaluation, or model interaction. Experience designing shared ML infrastructure or platforms used across multiple teams or applications. The compensation for this position also includes equity and benefits.

Machine Learning Engineer III Primary Location : Cincinnati, OH V-Soft Consulting is currently hiring for a Machine Learning Engineer III for our premier client in Cincinnati, OH. Education and Experience: 5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering. Demonstrated experience implementing end-to-end machine learning lifecycle management. Experience operating ML workloads in cloud environments (AWS, Azure, or GCP). Experience supporting both batch and real-time machine learning solutions. Experience working with large-scale structured and unstructured data. 3+ years of experience building and supporting production machine learning systems. Knowledge and Skills: ML Platform Engineering & Model Operations Design, develop, deploy, and manage production-grade machine learning solutions. Build scalable and reusable machine learning platforms that support model development, training, validation, deployment, and monitoring. MLOps Pipeline Development Design and maintain automated CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows. Automate model testing, validation, deployment, rollback, and retraining processes. Feature Store & Data Engineering Integration Design and support enterprise feature stores to improve feature reuse, consistency, and model governance. Collaborate with data engineering teams to integrate machine learning solutions with data warehouses, data lakes, and operational data sources. DevOps & Infrastructure Git/GitHub/GitLab Jenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD tools Terraform or infrastructure-as-code frameworks Linux administration Shell scripting Production Monitoring & Reliability Implement model monitoring frameworks covering: Model performance Data quality Concept drift Feature drift System health and reliability Data drift Interested? Qualified candidates should send their resumes to lpanda@vsoftconsulting.com V-Soft Consulting Group is recognized among the top 100 fastest growing staffing companies in North America, V-Soft Consulting Group is headquartered in Louisville, KY with strategic locations in India, Canada and the U.S. V-Soft is known as an agile, innovative technology services company holding several awards and distinctions and has a wide variety of partnerships across diverse technology stacks. As a valued V-Soft Consultant, you're eligible for full benefits (Medical, Dental, Vision), a 401(k) plan, competitive compensation and more. V-Soft is partnered with numerous Fortune 500 companies, exceptionally positioned to advance your career growth. V-Soft Consulting provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. For more information or to view all our open jobs, please visit www.vsoftconsulting.com or call (844) 425-8425.

Job Posting End Date: November 01 Our Fortune 500 company is driving a digital transformation and looking for forward-thinking innovators to disrupt how our industry thinks about and uses technology. As one of the world's leading employee benefits providers, we help millions of people gain affordable access to benefits that help them protect their families, their finances and their futures. Are you an asker of questions, a solver of problems, and a challenger of the status quo? Our mission is to provide a differentiated customer experience and exceed the expectations people have of technology at any company — not just insurers. We are seeking individuals to join our team of talented IT professionals who share never-ending passion and an unwavering focus on our customer experience. Team members comfortable working in an agile, fast-paced, and delivery-focused environment thrive in our environment where we value an entrepreneurial spirit and those who challenge the status-quo. Unum is changing, and we're excited about what's next. Join us. General Summary We're looking for a Senior MLOps Engineer to help build, scale, and evolve the platforms that take machine learning models from experimentation to reliable production systems. This is primarily an MLOps role with exposure to GenAI and agentic AI. You'll work closely with data science and engineering teams to maintain and improve existing ML platforms, automate the model lifecycle, strengthen deployment and monitoring capabilities, and build new MLOps and AgentOps capabilities. You'll also help establish technical standards and mentor other engineers as the organization continues to mature its ML and AI engineering practices. Job Specifications Bachelor's degree in Computer Science, Information Technology, Information Systems, a related field, or equivalent relevant work experience. 5+ years of experience in software engineering, DevOps, ML engineering, MLOps, or a related discipline, with demonstrated experience supporting machine learning systems in production. Strong Python programming skills and software engineering fundamentals. Hands-on production MLOps experience across CI/CD, model deployment, model serving, and monitoring. Hands-on experience with AWS and cloud-based infrastructure. Strong experience with Docker and Kubernetes. Experience with Infrastructure as Code, such as Terraform. Hands-on experience contributing to at least one LLM or agentic AI project, such as a proof of concept, pilot, or production implementation. Relevant experience may include RAG, evaluation, versioning, monitoring, tracing, cost management, or guardrails. Experience with ML lifecycle and platform technologies. Databricks and MLflow are preferred, although comparable production MLOps tools and platforms will be considered. Experience building or maintaining CI/CD pipelines. Experience with GitHub Actions is preferred. Experience with observability and monitoring technologies. AWS CloudWatch and Databricks Lakehouse Monitoring experience is preferred. Strong communication and collaboration skills with the ability to work effectively across data science and engineering teams. Ability to mentor other engineers, influence technical direction, and help establish engineering standards without formal people-management responsibility. Preferred Qualifications Hands-on experience with Databricks, including capabilities such as Unity Catalog, Jobs/Workflows, Model Serving, and Lakehouse Monitoring. Experience with MLflow for ML lifecycle management. Experience supporting GenAI or agentic AI solutions beyond initial experimentation. Experience with AI/agent observability, evaluation, tracing, versioning, cost management, or guardrails. Background as a software/DevOps engineer who transitioned into MLOps, or an ML engineer with strong infrastructure engineering experience. Experience owning or improving an ML platform at scale. Principal Duties And Responsibilities Design, build, maintain, and scale production ML pipelines and platforms supporting model deployment, serving, monitoring, and lifecycle management. Build and improve CI/CD workflows for ML models, including automated testing, versioning, deployment, and release processes. Develop and operate scalable infrastructure supporting real-time and batch model inference. Implement monitoring and observability for ML systems, including model performance, system health, reliability, and operational issues. Help expand MLOps capabilities into GenAI and AgentOps, including areas such as evaluation, versioning, monitoring, tracing, cost management, and guardrails. Maintain and improve infrastructure using Infrastructure as Code, including Terraform or comparable technologies. Support ML lifecycle capabilities such as experiment tracking, model registries, model serving, and ML workflow orchestration. Partner with data scientists and engineers to improve the reliability, scalability, and maintainability of production ML solutions. Drive continuous improvements to deployment processes, monitoring, platform reliability, and developer experience. Establish and promote engineering standards around reproducibility, security, governance, and operational excellence. Mentor other engineers, contribute to technical design decisions, and help establish MLOps engineering standards across teams. ~IN1 Our company is built on helping individuals and families, and this starts with our employees. We want employees to maintain a positive balance, which is why we provide access to the benefits and resources they need to invest in themselves. From our onsite fitness facilities and generous paid time off to employee professional development programs, we are committed to helping employees live and work their best – both inside and outside the office. Unum is an equal opportunity employer, considering all qualified applicants and employees for hiring, placement, and advancement, without regard to a person's race, color, religion, national origin, age, genetic information, military status, gender, sexual orientation, gender identity or expression, disability, or protected veteran status. The base salary range for applicants for this position is listed below. Unless actual salary is indicated above in the job description, actual pay will be based on skill, geographical location and experience. $89,400.00-$183,500.00 Additionally, Unum offers a portfolio of benefits and rewards that are competitive and comprehensive including healthcare benefits (health, vision, dental), insurance benefits (short & long-term disability), performance-based incentive plans, paid time off, and a 401(k) retirement plan with an employer match up to 5% and an additional 4.5% contribution whether you contribute to the plan or not. All benefits are subject to the terms and conditions of individual Plans. Company Unum

Company Overview At 4A Consulting, we turn complexity into opportunity. Founded in 2014 and headquartered in Baltimore, MD, we are a women-owned, boutique consulting firm specializing in delivering innovative, data-driven solutions to both the Federal Government and Fortune 500 clients. Our team blends deep industry knowledge with advanced technologies to design tailored strategies that drive measurable and sustainable outcomes. We pride ourselves on an agile, collaborative approach that helps organizations navigate challenges and seize emerging opportunities in a rapidly evolving digital landscape. At 4A, we don't just deliver projects, we build trusted partnerships that empower our clients to lead with confidence in the digital age. Position Overview The AI/ML Platform Engineer will develop, maintain, and optimize the infrastructure, tools, and automated workflows supporting the state\'s shared enterprise AI/ML platform. The role focuses on ML pipeline automation, CI/CD, model deployment, monitoring, cloud cost optimization, and developer enablement. The engineer will collaborate with data scientists, data engineers, software developers, agency stakeholders, and external partners to deliver reliable, scalable, and production-ready AI/ML solutions across state agencies. Key Responsibilities ML Pipeline Automation and CI/CD: Build and maintain automated ML pipelines for data preprocessing, feature engineering, model training, evaluation, and deployment. Implement automated testing, pipeline validation, staged deployment, and failure recovery using Apache Airflow, Kubeflow Pipelines, Prefect, or equivalent tools. Model Development and Deployment: Prototype, implement, and optimize ML algorithms and integrate models into production systems. Apply appropriate supervised and unsupervised learning, deep learning, NLP, computer vision, and Generative AI techniques based on business requirements. Monitoring and Reliability: Implement logging, metrics, dashboards, and alerts to monitor pipeline health, model performance, data drift, model degradation, and infrastructure utilization. Participate in production support, incident response, troubleshooting, and root-cause analysis. Cloud Cost Optimization: Monitor compute, storage, and data-transfer costs. Optimize resource utilization through autoscaling, spot or preemptible instances, and workload scheduling. Contribute to infrastructure-as-code configurations, resource tagging, and budget controls. Developer Experience: Maintain internal SDKs, self-service tools, onboarding documentation, and reusable templates. Support platform users, troubleshoot issues, resolve bugs, and conduct technical enablement sessions for agency teams. Collaboration and Continuous Improvement: Translate business requirements into technical solutions, collaborate across multidisciplinary teams, evaluate emerging ML technologies, and improve platform reliability, scalability, usability, and operational efficiency. Required Qualifications Bachelor\'s degree in Computer Science, Engineering, or a related technical field. A master\'s degree or PhD is preferred but not required. At least five (5) years of experience in software engineering, data engineering, or ML engineering, including at least two (2) years working with production ML systems. Strong proficiency in Python and experience developing maintainable, production-quality code. Solid understanding of ML and deep learning fundamentals, including model training, evaluation, feature engineering, versioning, and deployment. Experience with ML frameworks and libraries such as PyTorch, TensorFlow, or scikit-learn, and with at least one ML orchestration tool: Apache Airflow, Kubeflow Pipelines, or Prefect. Familiarity with AWS, Azure, or GCP and managed AI/ML services. Experience with Docker, basic Kubernetes operations, and CI/CD tools such as GitHub Actions, GitLab CI, or Jenkins. Experience building, deploying, monitoring, or troubleshooting ML pipelines and production workflows. Strong analytical, problem-solving, debugging, communication, and cross-functional collaboration skills. Preferred Qualifications Experience with monitoring and observability tools such as Prometheus, Grafana, or Datadog. Familiarity with experiment tracking and model management tools such as MLflow or Weights & Biases. Experience with infrastructure as code and configuration management using Terraform or Ansible. Familiarity with feature stores such as Feast or Tecton. Experience with internal developer tools, SDKs, and self-service platform capabilities. Knowledge of NLP, computer vision, speech processing, reinforcement learning, or Generative AI. Experience in government, regulated, or large enterprise environments. Skills and Competencies Strong attention to detail and commitment to operational reliability. Ability to independently troubleshoot complex pipelines and distributed systems. Clear technical documentation and incident reporting skills. Collaborative approach to working with engineers, data scientists, and nontechnical stakeholders. Continuous improvement mindset focused on reducing operational issues, deployment delays, and unnecessary cloud costs. Why Join 4A Be part of a mission-driven, women-owned consulting firm with a reputation for excellence. Work on high-impact projects across federal and commercial clients. Collaborate in an inclusive, growth-oriented culture where innovation is valued. Access career development programs, mentorship, and learning opportunities. Enjoy a comprehensive benefits package, flexible work options, and a focus on work-life balance. Equal Opportunity Statement 4A Consulting is an Equal Opportunity Employer committed to an inclusive hiring process. Applicants requiring a reasonable accommodation due to a disability may contact accommodations@4a-consulting.com. This email is intended solely for accommodation requests. Please note that 4A Consulting participates in the federal E-Verify program. Upon hire, we will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the US. 4A Consulting will only use E-Verify once you have accepted a job offer and completed the Form I-9.

Job Description Why GMF Technology? Innovation isn't just a talking point at GM Financial, it's how we operate. From generative AI and cloud-native technologies to peer-led learning and hackathons, our tech teams are building real solutions that make a difference. We're committed to AI-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry. Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact. This position will be posted until filled. About The Role Apply AI/ML Engineering, Data Science, and Data Engineering practices to design, develop, test, launch, and maintain ML and LLM Operations capabilities. This position requires an understanding of the Data Science model development and Gen AI/LLM driven workflow lifecycle, MLFlow features, and the benefits of AI/ML product development and deployment. A general understanding of cloud architectures, software development principles CI/CD, deployment, APIs, microservices, Data and Dev ops, and Event-driven architectures is highly desirable. The ability to deliver solutions that are based on a business understanding is essential. Key responsibilities include enabling ML model and LLM-driven automation, and deployment to meet operational requirements like scalability, robustness, reusability, reproducibility, compliance, and responsible AI. This position requires expertise and passion for working in agile teams to plan effectively, collaborating with broader cross-functional teams, and successfully deliver mission-critical Data and AI projects. The Senior Machine Learning Operations Engineer is the subject matter expert in MLOps and LLMOps. They have good understanding of tools and technologies used to manage AI and ML Model Lifecycles. Provide technical support and coach junior ML Ops engineers. Responsibilities In this role you will: Develop enterprise-wide and scalable cloud-based MLOps, LLMOps capabilities that span the full life cycle of Machine Learning, Deep Learning and Generative AI models Develop reusable, secure, and robust AI and ML pipelines, monitor model performance, monitor data drift, utilize insights to train models, enable automatic audit trails, LLM traces creation for all artifacts, deploy across a wide range of business applications, and sustain a high level of automation across all AI/ML lifecycle activities. This includes developing code and making sure that AI/ML models are production ready Continuously improve the speed, quality, and efficiency of model/experiments development, production, and maintenance Collaborate with Model Management/Governance to develop and maintain enterprise-wide MLOps and LLMOps standards Collaborate with internal stakeholders and vendors in developing MLOps and LLMOps solutions that meet business requirements across a variety of areas including, but not limited to, Data Science, IT, Cybersecurity, Compliance, and Legal Maintain up to date knowledge about the latest advances in AI and ML Engineering, engage stakeholders, and champion proactive measures to sustain a cost effective, efficient, and innovative MLOps and LLMOps capabilities Develop and maintain a deep understanding of business requirements to ensure that MLOps and LLMOps solutions deliver practical and timely value Conduct AI and ML Engineering research and proof of concept projects to improve practice and develop business cases that support business needs Develop and apply algorithms that generate success metrics to improve the value of MLOps and LLMOps business function Presents findings and analysis for use in decision making and demonstrate bottom-line financial benefits (ROI) Collaborate with Business Teams, Data Architects, Data Engineers, Data Science and Cloud Solution Architects in developing solutions Prioritizes tasks and meets project deadlines in a fast-paced work environment Qualifications What makes you an ideal candidate? Proven experience in Azure ecosystem across the full AI/ML lifecycle with end-to-end automation (MLOps, LLMOPs), including ingestion and validation, dataset/version management, lineage and metadata tracking, feature engineering, experiment tracking, training, evaluation, pipeline-based validation gates, Responsible AI reviews, model registration, containerized deployment, event-driven monitoring, and integration like MLflow on Databricks or Azure Machine Learning Hands-on experience operationalizing GenAI solutions, including LLM application patterns such as Deep Research/RAG, prompt/version management, embedding workflows, model and prompt evaluation, safety/content filtering, human-in-the-loop review, and production monitoring (Tracing and Evaluations) for quality, latency, and cost Ability to understand and clearly articulate trade-offs of various approaches to solving AI and machine learning platforms e.g., Databricks vs. Azure ML pipelines, batch vs. streaming, AKS vs. Managed Serving endpoints, cost vs. performance, governance vs. speed Experience with event-driven architecture and streaming using Azure Event Hubs and Azure Function Apps Working knowledge in Azure DevOps or equivalent including GitHub Repos, Boards, CI/CD and other related functionality Experience delivering in agile environments (preferably SAFe) and partnering effectively with Data Science, Data Engineering, Application Engineering, Data Privacy and Security teams Strong software engineering fundamentals (testing, code quality, packaging, reusable modules, infrastructure-as-code mindset, observability, and secure-by-default design) Experience with large-scale data and lakehouse ecosystems, including Azure Databricks, Spark, and Delta-lake engines; Strong quantitative, analytical and data interpretation skills with a solid foundation of mathematics, probability, and statistics Ability to program in Python and SQL Experience with cloud deployment and runtime operations using Docker and Azure Kubernetes Service (AKS); familiarity with GitOps and environment promotion patterns is a plus Experience building/hosting ML and GenAI services using Azure App Services and/or AKS (APIs, asynchronous workers, scheduled jobs), with attention to reliability and production readiness Familiarity with the Azure AI ecosystem, including Azure AI Foundry, AI Services, Azure ML, and enterprise governance for model access, secrets, and data protection Strong applied ML understanding (e.g., regression, tree-based methods, neural networks, NLP) and the ability to translate business problems into measurable metrics and experiments Experience with BI/analytics tools (especially Power BI) and awareness of Data and Analytics platforms such as Azure Databricks Rapid prototyping and experimentation using low-code/no-code GenAI tools (e.g., Microsoft Copilot Studio) to quickly validate use cases, iterate on conversational flows, and transition successful prototypes into production-grade implementations Experience accelerating daily workflows with GenAI coding assistants (e.g., Databricks Genie, GitHub Copilot) while maintaining engineering rigor through code review, testing, security controls, and CI/CD Strong communication skills with the ability to explain complex concepts to senior stakeholders and connect technical delivery to measurable business and financial outcomes Proven ability to manage multiple workstreams in a time-sensitive environment and collaborate to solve problems creatively Experience/certification in Azure, Azure AI, ML, Databricks, or equivalent such as AWS and GCP, MLOps, Data architecture, container orchestration, streaming, H2O, Spark, and Linux Knowledge of Transactional, analytical databases and Data analysis techniques for structured, semi-structured, and unstructured data modeling and analysis (RDBMS, Columnar data-Parquet, JSON, etc.) Broad knowledge of networking concepts including TCP/IP, subnetting, routing, DHCP, and others is desirable Understanding of cybersecurity principles Work Experience & Education 4+ years as Data Scientist, Machine Learning Engineer, MLOps Engineer, AI Engineer or similar quantitative field required Azure Databricks experience preferred High School Diploma or equivalent required Master's Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline required PhD preferred What We Offer : Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays. Our Culture : Our team members define and shape our culture — an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work — we thrive. Compensation : Competitive pay and bonus eligibility. Work Life Balance : Flexible hybrid work environment, 2-days a week in office. NOTE: We are unable to consider candidates who require visa sponsorship for this position This position is not open to agency submissions #GMFJobs

Description Please note: This role is contingent upon a contract award. While it is not an immediate opening, we are actively conducting interviews and extending offers in anticipation of the award. The Work At ICF our Digital Modernization Division is an information technology and management consulting organization that delivers integrated, strategic solutions to federal clients. We bring expertise in cloud, cybersecurity, enterprise architecture, data modernization, and digital transformation to support mission-critical government programs. Join a team accelerating the modernization of a large federal agency's enterprise data and analytics ecosystem. This cloud-based platform provides data storage, analytics, governance, and AI/ML capabilities that enable thousands of users to transform data into actionable insights. As demand continues to grow, the team is focused on migrating legacy workloads, streamlining onboarding and support, expanding platform capabilities, and helping organizations across the enterprise adopt modern data and AI solutions at scale. The ML Ops Engineer will develop and maintain machine learning pipelines, model registries, deployment frameworks, monitoring solutions, and automated workflows that support enterprise AI initiatives. Working closely with data scientists, data engineers, cloud architects, platform engineers, and Trustworthy AI stakeholders, this role enables the successful operationalization of machine learning capabilities across cloud-based environments. Job Location: Remote, however, strong preference for candidates who live in the Washington DC Metro Area. There will be occasional onsite meetings on the client site in Washington, DC. If you accept this position, you should note that ICF does monitor employee work locations, blocks access from foreign locations/foreign IP addresses, and prohibits personal VPN connections. What You'll Do Design, build, and maintain production-grade MLOps pipelines for model training, evaluation, validation, registration, deployment, monitoring, retraining, rollback, and retirement. Develop reusable MLOps and LLMOps patterns using Azure Databricks, Databricks ML, MLflow, Unity Catalog, Azure Machine Learning, Python, and approved Azure AI services. Implement experiment tracking, model versioning, model registries, lineage, quality metrics, model cards, and approval workflows using MLflow and Unity Catalog to ensure models are discoverable, reusable, and auditable. Build automated CI/CD pipelines using GitHub Actions, Azure DevOps, Databricks Asset Bundles, and Terraform to promote models, code, configurations, and supporting infrastructure consistently across development, test, and production environments. Deploy and maintain scalable model-serving infrastructure for batch, streaming, real-time, and API-based inference workloads using Databricks Model Serving, Azure Machine Learning, Databricks compute, serverless capabilities, and GPU-enabled configurations, as applicable. Establish automated testing and evaluation gates for model performance, data quality, fairness, safety, explainability, robustness, privacy, and release readiness. Implement model and feature monitoring, drift and bias detection, logging, tracing, alerting, and operational dashboards using MLflow Model Monitoring, Lakehouse Monitoring, and the broader platform monitoring stack. Develop automated retraining, champion/challenger promotion, rollback, and model-retirement workflows that prevent performance degradation and support reliable production operations. Integrate ML workflows with source control, infrastructure provisioning, secrets management, identity and access controls, environment management, and enterprise release processes. Implement Trustworthy AI controls, including model documentation, impact assessments, audit logging, content safeguards, human oversight, and evaluation evidence aligned with Government-approved standards and the NIST AI Risk Management Framework. Partner with data scientists, AI engineers, data engineers, platform engineers, DevSecOps, Security, and Governance personnel to make models repeatable, testable, deployable, and operationally supportable. Monitor compute and serving utilization, tune cluster and endpoint configurations, apply tagging conventions, and implement cost-optimization strategies for training and inference workloads. Troubleshoot failed pipelines, deployment issues, model-serving incidents, performance degradation, and monitoring alerts while supporting root-cause analysis and corrective actions. Create and maintain deployment templates, reusable repositories, operational runbooks, model documentation, release evidence, and technical standards that accelerate responsible AI/ML adoption across the enterprise. Basic Qualifications U.S. Citizenship is required due to federal contract requirements. Candidate must reside in the U.S., be authorized to work in the U.S., and all work must be performed in the U.S. Candidate must have lived in the U.S. for three (3) full years out of the last five (5) years. Bachelor's degree in Computer Science, Software Engineering, Machine Learning, Data Science, Engineering, or a related field or a high school diploma with four (4) additional years of relevant experience in lieu of a bachelor's degree. 6+ years of experience designing and supporting production machine learning solutions in cloud environments. Master's degree may substitute for two (2) years of relevant experience. Preferred Qualifications Experience with machine learning operations (MLOps), automated deployment pipelines, and model lifecycle management. Proficiency in Python and modern software development practices. Experience with source control systems and CI/CD frameworks. Hands-on experience with Azure Machine Learning, Databricks, MLflow, or comparable ML platforms. Experience implementing model monitoring, automated retraining, and performance tracking solutions. Knowledge of cloud-native architecture, containerization, and scalable model serving patterns. Experience supporting Responsible AI, AI governance, or Trustworthy AI initiatives. Familiarity with infrastructure-as-code and automated environment provisioning. Strong understanding of cloud cost optimization practices for AI/ML workloads. #Indeed Bot and Third-Party Applications Please note that this application must be submitted directly by the applicant for consideration. Failure to do so may result in the application being excluded for consideration. Applicants needing an accommodation for disability or religious purposes in connection with the application process should contact Candidateaccommodation@icf.com for assistance. Working at ICF ICF is a global advisory and technology services provider, but we're not your typical consultants. We combine unmatched expertise with cutting-edge technology to help clients solve their most complex challenges, navigate change, and shape the future. We can only solve the world's toughest challenges by building a workplace that allows everyone to thrive. We are an equal opportunity employer. Together, our employees are empowered to share their expertise and collaborate with others to achieve personal and professional goals. For more information, please read our EEO policy. We will consider for employment qualified applicants with arrest and conviction records. Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process. To request an accommodation, please email Candidateaccommodation@icf.com and we will be happy to assist. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. Read more about workplace discrimination rights or our benefit offerings which are included in the Transparency in (Benefits) Coverage Act. Candidate AI Usage Policy At ICF, we are committed to ensuring a fair interview process for all candidates based on their own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) tools to generate or assist with responses during interviews (whether in-person or virtual) is not permitted. This policy is in place to maintain the integrity and authenticity of the interview process. However, we understand that some candidates may require accommodation that involves the use of AI. If such an accommodation is needed, candidates are instructed to contact us in advance at candidateaccommodation@icf.com. We are dedicated to providing the necessary support to ensure that all candidates have an equal opportunity to succeed. Pay Range There are multiple factors that are considered in determining final pay for a position, including, but not limited to, relevant work experience, skills, certifications and competencies that align to the specified role, geographic location, education and certifications as well as contract provisions regarding labor categories that are specific to the position. The pay range for this position based on full-time employment is: $98,614.00 - $167,644.00 Nationwide Remote Office (US99)

Description Please note: This role is contingent upon a contract award. While it is not an immediate opening, we are actively conducting interviews and extending offers in anticipation of the award. The Work At ICF our Digital Modernization Division is an information technology and management consulting organization that delivers integrated, strategic solutions to federal clients. We bring expertise in cloud, cybersecurity, enterprise architecture, data modernization, and digital transformation to support mission-critical government programs. Join a team accelerating the modernization of a large federal agency's enterprise data and analytics ecosystem. This cloud-based platform provides data storage, analytics, governance, and AI/ML capabilities that enable thousands of users to transform data into actionable insights. As demand continues to grow, the team is focused on migrating legacy workloads, streamlining onboarding and support, expanding platform capabilities, and helping organizations across the enterprise adopt modern data and AI solutions at scale. The ML Ops Engineer will develop and maintain machine learning pipelines, model registries, deployment frameworks, monitoring solutions, and automated workflows that support enterprise AI initiatives. Working closely with data scientists, data engineers, cloud architects, platform engineers, and Trustworthy AI stakeholders, this role enables the successful operationalization of machine learning capabilities across cloud-based environments. Job Location: Remote, however, strong preference for candidates who live in the Washington DC Metro Area. There will be occasional onsite meetings on the client site in Washington, DC. If you accept this position, you should note that ICF does monitor employee work locations, blocks access from foreign locations/foreign IP addresses, and prohibits personal VPN connections. What You'll Do Design, build, and maintain production-grade MLOps pipelines for model training, evaluation, validation, registration, deployment, monitoring, retraining, rollback, and retirement. Develop reusable MLOps and LLMOps patterns using Azure Databricks, Databricks ML, MLflow, Unity Catalog, Azure Machine Learning, Python, and approved Azure AI services. Implement experiment tracking, model versioning, model registries, lineage, quality metrics, model cards, and approval workflows using MLflow and Unity Catalog to ensure models are discoverable, reusable, and auditable. Build automated CI/CD pipelines using GitHub Actions, Azure DevOps, Databricks Asset Bundles, and Terraform to promote models, code, configurations, and supporting infrastructure consistently across development, test, and production environments. Deploy and maintain scalable model-serving infrastructure for batch, streaming, real-time, and API-based inference workloads using Databricks Model Serving, Azure Machine Learning, Databricks compute, serverless capabilities, and GPU-enabled configurations, as applicable. Establish automated testing and evaluation gates for model performance, data quality, fairness, safety, explainability, robustness, privacy, and release readiness. Implement model and feature monitoring, drift and bias detection, logging, tracing, alerting, and operational dashboards using MLflow Model Monitoring, Lakehouse Monitoring, and the broader platform monitoring stack. Develop automated retraining, champion/challenger promotion, rollback, and model-retirement workflows that prevent performance degradation and support reliable production operations. Integrate ML workflows with source control, infrastructure provisioning, secrets management, identity and access controls, environment management, and enterprise release processes. Implement Trustworthy AI controls, including model documentation, impact assessments, audit logging, content safeguards, human oversight, and evaluation evidence aligned with Government-approved standards and the NIST AI Risk Management Framework. Partner with data scientists, AI engineers, data engineers, platform engineers, DevSecOps, Security, and Governance personnel to make models repeatable, testable, deployable, and operationally supportable. Monitor compute and serving utilization, tune cluster and endpoint configurations, apply tagging conventions, and implement cost-optimization strategies for training and inference workloads. Troubleshoot failed pipelines, deployment issues, model-serving incidents, performance degradation, and monitoring alerts while supporting root-cause analysis and corrective actions. Create and maintain deployment templates, reusable repositories, operational runbooks, model documentation, release evidence, and technical standards that accelerate responsible AI/ML adoption across the enterprise. Basic Qualifications U.S. Citizenship is required due to federal contract requirements. Candidate must reside in the U.S., be authorized to work in the U.S., and all work must be performed in the U.S. Candidate must have lived in the U.S. for three (3) full years out of the last five (5) years. Bachelor's degree in Computer Science, Software Engineering, Machine Learning, Data Science, Engineering, or a related field or a high school diploma with four (4) additional years of relevant experience in lieu of a bachelor's degree. 6+ years of experience designing and supporting production machine learning solutions in cloud environments. Master's degree may substitute for two (2) years of relevant experience. Preferred Qualifications Experience with machine learning operations (MLOps), automated deployment pipelines, and model lifecycle management. Proficiency in Python and modern software development practices. Experience with source control systems and CI/CD frameworks. Hands-on experience with Azure Machine Learning, Databricks, MLflow, or comparable ML platforms. Experience implementing model monitoring, automated retraining, and performance tracking solutions. Knowledge of cloud-native architecture, containerization, and scalable model serving patterns. Experience supporting Responsible AI, AI governance, or Trustworthy AI initiatives. Familiarity with infrastructure-as-code and automated environment provisioning. Strong understanding of cloud cost optimization practices for AI/ML workloads. #Indeed Bot and Third-Party Applications Please note that this application must be submitted directly by the applicant for consideration. Failure to do so may result in the application being excluded for consideration. Applicants needing an accommodation for disability or religious purposes in connection with the application process should contact Candidateaccommodation@icf.com for assistance. Working at ICF ICF is a global advisory and technology services provider, but we're not your typical consultants. We combine unmatched expertise with cutting-edge technology to help clients solve their most complex challenges, navigate change, and shape the future. We can only solve the world's toughest challenges by building a workplace that allows everyone to thrive. We are an equal opportunity employer. Together, our employees are empowered to share their expertise and collaborate with others to achieve personal and professional goals. For more information, please read our EEO policy. We will consider for employment qualified applicants with arrest and conviction records. Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process. To request an accommodation, please email Candidateaccommodation@icf.com and we will be happy to assist. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. Read more about workplace discrimination rights or our benefit offerings which are included in the Transparency in (Benefits) Coverage Act. Candidate AI Usage Policy At ICF, we are committed to ensuring a fair interview process for all candidates based on their own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) tools to generate or assist with responses during interviews (whether in-person or virtual) is not permitted. This policy is in place to maintain the integrity and authenticity of the interview process. However, we understand that some candidates may require accommodation that involves the use of AI. If such an accommodation is needed, candidates are instructed to contact us in advance at candidateaccommodation@icf.com. We are dedicated to providing the necessary support to ensure that all candidates have an equal opportunity to succeed. Pay Range There are multiple factors that are considered in determining final pay for a position, including, but not limited to, relevant work experience, skills, certifications and competencies that align to the specified role, geographic location, education and certifications as well as contract provisions regarding labor categories that are specific to the position. The pay range for this position based on full-time employment is: $98,614.00 - $167,644.00 Nationwide Remote Office (US99)

About AiDASH AiDASH is leading the PreventionFirst™movement for electric utilities and transforming grid resilience through its pioneering platform that unifies vegetation, asset, storm, and wildfire intelligence. Powered by SatelliteFirst™ Inspection & Monitoring, AiDASH delivers comprehensive visibility across the entire grid at the right frequency and budget, using the right data modality. More than 200 customers trust AiDASH to keep the lights on, spend where it counts, and defend every decision, Securing Tomorrow across every mile of the grid. Learn more at www.aidash.com. The PreventionFirst movement is growing, and so is the recognition behind it. In 2026, Forbes named AiDASH one of America's Best Startup Employers for the 4th consecutive year, and TIME included AiDASH among America's Top GreenTech Companies for the 3rd year in a row. Deloitte Technology Fast 500™ ranked AiDASH No. 12 in the San Francisco Bay Area, and No. 59 overall in their selection of the top 500 for 2024. Join us in Securing Tomorrow Together! The Role Our satellite and AI-powered products are changing how utilities see and protect the grid, and we're gearing up for aggressive customer growth over the next two years. That growth runs on AI at scale. We're building a brand-new Data Inference AI Pipeline team to deliver it, and we're looking for a Staff MLOps Engineer to architect its operational backbone from the very first line of infrastructure code. As the very first engineer on this team , you'll start with a blank canvas and a rare chance to build something that lasts. You'll shape how our models are deployed, how the pipeline scales and recovers under load, and what it costs to run. Just as importantly, you'll help define the culture, engineering standards, and ways of working that every future teammate inherits. This is a hands-on, high-leverage individual contributor role: your decisions will set the foundation the entire team builds on, and you'll be the go-to technical authority on MLOps as the team grows. You'll report to the Director of Engineering for the Data Inference AI Pipeline team. Our pending acquisition by Schneider Electric will accelerate our mission as we bring our combined offering to our joint customer base, and the infrastructure you build will power outcomes at an even greater scale. Location: This is a hybrid role based in Palo Alto, CA, requiring 2 days per week in the office. How you'll make an impact: Deployment Own the model deployment pipeline end-to-end: packaging, versioning, rollout, and rollback for ML models moving from training to production Build and maintain deployment infrastructure on SageMaker (endpoints, batch transform, multi-model/multi-container hosting), and evaluate where alternatives such as self-hosted serving or other managed options are a better fit Set the CI/CD pattern for model releases, including automated testing, staged rollout, and canary/shadow deployments Scalability & elasticity Design inference infrastructure that scales seamlessly with real-time and batch demand Right-size compute (CPU/GPU/Inferentia or equivalent) for each model and workload to balance latency and cost Load-test the pipeline and build capacity plans that stay ahead of customer growth milestones Cost Own the cost-per-inference metric: instrument it, report on it, and drive it down as volume scales Build cost-awareness into architecture decisions, including autoscaling policies, spot/on-demand mix, and model size/quantization trade-offs Failover & resilience Design for graceful degradation and failover across regions and availability zones for inference serving Define and test disaster-recovery procedures, and run regular failure-injection and chaos exercises Build monitoring, alerting, and on-call runbooks for model-serving infrastructure, covering drift, latency SLOs, error rates, and infra health Cross-team leadership Partner with Data Science to bring new models to production on a platform built to support them Partner with the Web Application team where inference results power customer-facing features, keeping latency and reliability expectations aligned Serve as the technical authority on MLOps practices as the Data Inference AI Pipeline team grows What success looks like in your first 6 months: Model deployment is a repeatable, automated process across models Inference infrastructure has proven it scales up and down smoothly under real load Cost-per-inference is measured, visible, and trending down as volume grows A tested failover and disaster-recovery plan is in place for the inference pipeline, with at least one failure-injection exercise completed Data Science can ship new models into production quickly and independently What we're looking for: Minimum Qualifications 7-10+ years in software/ML engineering, including 3+ years focused specifically on MLOps or ML infrastructure Hands-on production experience with AWS SageMaker (or an equivalent managed ML platform) for model deployment and serving A proven track record designing systems for elasticity/autoscaling, cost optimization, and failover/resilience at production scale Strong expertise in containerization and orchestration (Docker, Kubernetes) and in CI/CD built for ML workloads, including model versioning and automated testing and rollout Experience instrumenting and owning cost and performance metrics for the infrastructure you're responsible for The ability to set technical direction as a Staff-level IC, building buy-in across Data Science and other engineering teams High ownership mindset, low ego, and a collaborative spirit, with a passion for building platforms that help others ship faster Preferred Qualifications Experience with large imagery, video, or 3D sensor and point-cloud data pipelines or other high-volume unstructured data at scale Experience with multimodal data (imagery, sensor/time-series, geospatial) beyond purely textual or tabular data Experience building MLOps practices from scratch on a new team Familiarity with model monitoring and drift-detection tooling, and GPU cost-optimization techniques (quantization, batching, Inferentia/Trainium or equivalent) A background in a regulated or enterprise B2B domain such as utilities, energy, infrastructure, healthcare, or finance Relevant AWS/Kubernetes certifications or open-source MLOps tooling contributions What you'll love: Comprehensive Medical, Dental, and Vision Coverage: 100% coverage for employees and 80% for their spouses and children Health Reimbursement Account (HRA): 100% funded by AiDASH to cover medical deductibles 401(k) Plan: Begin contributing after two months of employment to prepare for your future. Currently, no company match is offered Parental Leave: Supportive parental leave with 16 weeks for primary caregivers and 4 weeks for secondary caregivers Generous Vacation Policy: Accrue 20 vacation days per year, plus enjoy an additional flex holiday to celebrate whatever feels most important to you Winter Break: From December 25th through January 1st, we give everyone time off to recharge and enjoy time with family and friends! We are proud to be an equal-opportunity employer. We are committed to embracing diversity and inclusion in our hiring practices, and we promote a work environment where everyone, from any race, color, religion, sex, sexual orientation, gender identity, or national origin, can do their best work. We offer a competitive annual pay range of $230,000 to $270,000 for this full-time position, which includes a base salary and bonus based on performance. This range reflects the anticipated annual pay range (base + bonus) for new hires. We strive to ensure our compensation packages are equitable and aligned with industry standards. Your recruiter can share more about compensation during the hiring process. We are committed to providing an inclusive and accessible interview experience for all candidates. Please let us know if you require any accommodation during the interview process, and we will make every effort to meet your needs. Read our Privacy Policy here: https://www.aidash.com/policy/privacy-policy/

Job Description Why GM Financial? GM Financial is the wholly owned captive finance subsidiary of General Motors and is headquartered in Fort Worth, U.S. We are a global provider of auto finance solutions, with operations in North America, South America, and the Asia Pacific region. Through our long-standing relationships with auto dealers, we offer attractive retail financing and lease programs to meet the needs of each customer. We also offer commercial lending products to dealers to help them finance and grow their businesses. At GM Financial, our team members define and shape our culture — an environment that welcomes new ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work — we thrive. Our Purpose: We pioneer the innovations that move and connect people to what matters. About This Role You will design, build, and operate cloud-based MLOps capabilities that support the full lifecycle of analytical and generative AI models. This role blends machine learning engineering, data engineering, and software engineering, with a strong focus on automation, scalability, governance, and production readiness. You'll work with technologies such as MLflow, Databricks, Azure Machine Learning, CI/CD pipelines, containerization, and event-driven architectures, partnering closely with data science, IT, and business teams to deliver secure, compliant, and high-impact AI solutions. This position will be posted until filled. Responsibilities What makes you an ideal candidate: Build and maintain scalable, cloud-based MLOps platforms supporting analytical and GenAI models end to end Develop production-ready ML pipelines for training, deployment, monitoring, governance, and lifecycle automation Improve speed, quality, and reliability of model development, experimentation, and operations Partner with model governance, security, and compliance teams to define and enforce MLOps standards Collaborate with data science, engineering, and business stakeholders to deliver solutions aligned to business needs Research, prototype, and evolve MLOps capabilities to drive innovation and measurable business value Qualifications Experience & Education 2-4 years as Data Scientist or machine learning engineer or similar quantitative field required High School Diploma or equivalent required Master's Degree in the field of Computer Science/Engineering, Analytics, Mathematics, or related discipline preferred, PhD preferred Proven hands-on experience across the full ML/MLOps lifecycle, including MLflow and platforms such as Databricks, Azure ML, or SageMaker Experience operationalizing GenAI solutions, including LLM patterns (e.g., RAG), prompt/version management, evaluation, safety, and monitoring Strong software and cloud engineering fundamentals, including CI/CD, containerization (Docker), and Kubernetes (AKS) Experience with event-driven and streaming architectures and modern cloud-native design patterns Advanced skills with Python, SQL, and large-scale data platforms (e.g., Spark, Delta, lakehouse architectures) Ability to clearly communicate technical trade-offs and connect AI delivery to business and financial outcomes What We Offer: Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays. Our Culture: Our team members define and shape our culture — an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work — we thrive. Compensation: Competitive pay and bonus eligibility. Work Life Balance: Flexible hybrid work environment, 2-days a week in office. NOTE: We are unable to consider candidates who require visa sponsorship for this position This position is not open to agency submissions #GMFJobs

Who We Are: At OPENLANE we make wholesale easy so our customers can be more successful. We're a technology company building the world's most advanced—and uncomplicated—digital marketplace for used vehicles. We're a data company helping customers buy and sell smarter with clear, actionable insights they can understand and use. And we're an innovation company accelerating the future of wholesale remarketing through curiosity, collaboration, and an entrepreneurial spirit. Our Values: Driven Waybuilders. We pursue challenges that inspire us to build, create, and innovate. Relentless Curiosity. We seek to understand and improve our customers' experience. Smart Risk-Taking. We transform risk into progress through data, experience, and intuition. Fearless Ownership. We deliver what we promise and learn along the way. We're Looking For An Machine Learning Operations (MLOps) Engineer who builds and operates the pipelines, platforms, and observability that carry machine learning models from experimentation into reliable, cost-efficient production. You will join a new MLOps team within Site Reliability Engineering and help define how OPENLANE trains, ships, monitors, and safely updates models at scale. You will independently design automated ML delivery workflows, reduce operational toil, and ensure models are reproducible, monitored, and serving predictions safely in production. You Are Analytical, pragmatic, proactive, collaborative, quality-focused, and AI-forward. You treat AI as a first-class engineering tool, critically evaluate its output, and apply strong engineering judgment to systems that must stay healthy long after the first deployment. You Bring Core knowledge of system architecture, distributed systems, cloud-native design, networking, and agile development methodologies (Scrum, Kanban). Functional expertise in designing end-to-end ML pipelines (data ingestion, training, validation, deployment), including requirements for latency, redundancy, scalability, and error handling. Technical expertise in CI/CD for ML, infrastructure-as-code, containerization and orchestration, and metrics, monitoring, and alerting toolsets. Working understanding of the model lifecycle: experiment tracking and lineage, model registries, model drift, data quality, and retraining triggers, with enough ML fluency to diagnose model issues alongside data scientists. Strong Python skills and the ability to write clean, well-tested, production-ready code. You Own Design, build, and operation of automated ML pipelines covering data ingestion, training, validation, deployment, and rollback. Model CI/CD, including automated testing and canary and rollback strategies for safe releases. Monitoring and observability for model performance, data drift, inference latency, and availability, with actionable alerting and incident response. Reproducibility of model workloads through versioned code, data, features, and environments. Identification and reduction of operational toil through automation, standardization, and reusable ML platform components. Documentation, testing, and debugging of pipelines and serving infrastructure, maintaining high code and design standards. You Drive Execution: Translate data science and product needs into scalable, automated ML delivery designs, and implement technical roadmap projects for the MLOps platform. Results: Shorten time-to-production for models while improving reliability, minimizing failure risk, and optimizing the cost, scaling, and utilization of training and inference workloads in partnership with Infrastructure teams. Team: Support hiring, knowledge sharing, and mentorship as the MLOps practice grows. How You'll Use AI in This Role AI-assisted coding, refactoring, and test generation for pipelines and platform tooling. AI-supported debugging and root-cause analysis of pipeline failures and model degradation. AI-assisted documentation and runbooks to reduce repetitive work and increase engineering leverage. Who You Will Work With Reporting to the Director, Site Reliability Engineering, this role collaborates daily with Data Scientists, Data Engineers, Software Engineers, Infrastructure, Security, Product Owners, and SRE teams across the organization. Where You Work Your work is performed as a Remote employee, with occasional team alignment meetings as needed. Must Have's 3+ years of experience in MLOps, ML engineering, Site Reliability Engineering, DevOps, or Software Engineering, including experience building or operating ML pipelines in production. Bachelor's degree preferred or equivalent practical experience. Strong Python skills and hands-on experience with a major cloud platform (AWS preferred). Experience with workflow orchestration (e.g., Airflow, Kubeflow, Step Functions), infrastructure-as-code (e.g., Terraform), and CI/CD systems. Experience with containers and Kubernetes (Docker, EKS preferred). Familiarity with model serving frameworks (e.g., TorchServe, TF Serving, Triton, BentoML, SageMaker endpoints) and monitoring tools (e.g., Prometheus, Grafana, Evidently AI). Hands-on experience with system debugging, observability, and incident response in production environments. Actively uses AI development tools and can critically evaluate AI outputs. Nice to Have's Experience with ML platforms and tooling such as SageMaker, MLflow, Databricks, or Vertex AI (GCP). Experience with feature stores, model registries, or LLM/GenAI deployment and evaluation (LLMOps). Experience with GPU workload scheduling and inference cost optimization. Experience interviewing candidates, conducting technical debriefs, and mentoring junior engineers or interns. Proven ability to acquire deep domain and architectural knowledge within 6 months of joining a team. What We Offer: Competitive pay Medical, dental, and vision benefits with employer HSA contributions (US) and FSA options (US) Immediately vested 401K (US) or RRSP (Canada) with company match Paid Vacation, Personal, and Sick Time Paid maternity and paternity leave (US) Employer-paid short-term disability, long-term disability, life insurance, and AD&D (US) Robust Employee Assistance Program Employer paid Leap into Service Day to volunteer Tuition Reimbursement for eligible programs Opportunities to expand your skill set and share your knowledge across a publicly traded, global organization Company culture of internal promotions, diverse career paths, and meaningful advancement Sound like a match? 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