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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)

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? Apply Now - We can't wait to hear from you!

About The Role The role owns the infrastructure that turns experimental ML work into reliable production systems - CI/CD for models, scalable training infrastructure, real-time and batch serving, and the observability tooling that keeps it all running. You will work directly with ML engineers and data scientists to eliminate the friction between 'it works in a notebook' and 'it works at scale', owning the platform decisions that determine how fast models ship and how reliably they perform. Key Responsibilities Design and maintain CI/CD pipelines for ML workflows using tools like Kubeflow, MLflow, or Argo Workflows, from model versioning through automated deployment Build and operate model serving infrastructure on Kubernetes, including GPU scheduling, autoscaling, canary deployments, and rollback strategies Implement monitoring and observability for production models: latency, throughput, data drift detection, and automated alerting with tools like Prometheus, Grafana, and Evidently Develop feature stores and data pipeline integrations (Airflow, dbt, Spark) ensuring consistency between training and serving environments Optimize training and inference costs across cloud providers (AWS, GCP, or Azure), including GPU utilization, spot instance strategies, and model quantization where appropriate Establish MLOps best practices across teams: model registries, experiment tracking, reproducibility standards, and infrastructure-as-code with Terraform Partner with ML engineers and data scientists to debug production incidents, profile performance bottlenecks, and improve deployment velocity What We Are Looking For 3-6 years of experience in MLOps, DevOps, or platform engineering, with at least 2 years supporting ML systems in production Strong proficiency with Kubernetes, Docker, and cloud infrastructure (AWS, GCP, or Azure) in production environments Hands-on experience with ML tooling: MLflow, Kubeflow, SageMaker, Vertex AI, or equivalent model deployment and tracking platforms Solid software engineering skills in Python and/or Go, including infrastructure-as-code experience with Terraform or Pulumi Experience building observability for distributed systems: metrics, logging, tracing, and drift monitoring for ML workloads Bachelor's degree in Computer Science, Engineering, or equivalent practical experience Bonus: Experience with GPU scheduling (NVIDIA device plugins, Slurm), LLM inference optimization (vLLM, TensorRT), or building internal ML platforms used by multiple teams

AI & Tech Breadcrumb Home > Generative AI Consultant Generative AI Consultant 415 Mission Street, 94105 San Francisco, United States I'm interested Share this job Company Description About Sia Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We're optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity. With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes. Sia's AI & Data Business Unit is the powerhouse of our firm's innovation—merging cutting-edge Data Science, Generative AI, and advanced digital solutions to transform industries. With over 350 experts worldwide, we tackle projects from proof-of-concept to large-scale deployment, always pushing the boundaries of AI capabilities. Our 12 R&D labs in Europe and North America drive continuous research in areas like computer vision, MLOps, and deep learning, partnering closely with our business consultants for real-world impact. By joining Sia's AI & Data team, you'll step into a vibrant, collaborative environment that nurtures professional growth and empowers you to shape the future of AI-driven consulting. Job Description Join us as an experienced Generative AI Specialist designing and implementing cutting-edge GenAI/LLM solutions across diverse industries. You'll act as a vital bridge between technical teams (Data Science, ML, Platform) to deliver business-centric AI value. You'll guide clients on the optimal path—using techniques like RAG, agents, or fine-tuning—for cost-effective impact. Your responsibilities extend beyond prompting to architecting robust AI products via benchmarking, prototyping, and validation. You'll orchestrate the full AI workflow, ensuring seamless model integration optimized for performance, security, and scale, while tackling infrastructure challenges. We provide extensive training to support your success. If you're driven to push AI boundaries and help clients rapidly adopt GenAI with confidence, apply to make a real difference. Responsibilities LLM/GenAI System Development: Design, build, train, fine-tune, and deploy sophisticated AI models leveraging LLMs (e.g., GPT-x, Claude, Gemini, Llama, Mistral) and other generative techniques. Assist in Solution Architecture: Support the GenAI Solution Architect in designing robust, scalable, and secure applications. Application Development: Develop applications powered by GenAI models (both self-managed and API-accessible) that meet business needs and comply with applicable regulations (GDPR, EU AI Act, model licenses, etc.). Advanced Prompt Engineering: Design and optimize effective prompts (e.g., few-shot, Chain/Tree/Graph of Thought, ReAct, Self-reflection, guardrails), balancing simplicity and complexity to enhance analytical capabilities, refine outputs, improve user experience, and control interactions. RAG Implementation: Design and implement Retrieval-Augmented Generation (RAG) architectures to improve accuracy and relevance by retrieving information from pre-determined knowledge sources, providing traceability (source attribution). Model Selection & Fine-Tuning: Select and fine-tune appropriate models (including multimodal - VLM, SLM - Visual Language Models, Small Language Models) to create higher-quality content (text, image, audio, code, etc.) and maximize business value creation opportunities. Integration & Deployment (MLOps): Implement MLOps best practices for the GenAI lifecycle, including automated pipelines (CI/CD), versioning, monitoring, and maintenance in production environments (Cloud platforms like AWS, Azure, GCP). Ensure seamless integration into existing systems and with external tools/APIs, potentially utilizing standardized protocols (MCP). Evaluation & Responsible AI: Develop and execute rigorous evaluation frameworks to measure model performance, reliability, fairness, and safety. Ensure adherence to Responsible AI principles and help teams and clients navigate end-to-end security and compliance processes. Research & Innovation: Stay abreast of the latest advancements in GenAI techniques, technologies, and frameworks. Experiment with new approaches and contribute to internal knowledge sharing. Collaboration: Work effectively within cross-functional teams, communicating complex technical concepts clearly to diverse stakeholders (both technical and non-technical). Documentation: Document processes, methodologies, and best practices for knowledge sharing and future reference. Use Case Differentiation: Distinguish between use cases suited for Generative AI versus traditional NLP applications (e.g., NER, sentiment analysis). Qualifications Education : Master's degree or Engineering degree (or equivalent) in Computer Science, Engineering, Data Science, or a related quantitative field. Experience: 1-3+ years of hands-on experience in machine learning, software engineering, or data science, with demonstrated experience on complex AI projects specifically involving LLMs and Generative AI. Required Technical Skills Strong programming skills, particularly in Python. Proficiency in at least one major GenAI application framework (LangChain, LlamaIndex, etc.). Proven experience in NLP/NLU, vector embeddings, and semantic search. Hands-on experience with fine-tuning LLMs. Mastery of advanced prompting techniques (Chain/Tree/Graph of Thought, ReAct, etc.). Knowledge of both proprietary and open-source model ecosystems (OpenAI, Anthropic, Mistral AI, Hugging Face, models hosted on AWS/Azure/GCP, etc.). Experience with Cloud platforms (AWS, Azure, or GCP) and their associated MLOps/AI services. Familiarity with MLOps principles, CI/CD tools, Docker, and Git. Soft Skills Excellent communication skills (written and verbal), ability to explain complex technical concepts simply. Strong analytical and complex problem-solving abilities. Team player with the ability to collaborate effectively with diverse profiles. Curiosity, pioneering spirit, and a demonstrated commitment to continuous learning. Preferred Qualifications Experience building agentic AI systems (e.g., LangGraph, AutoGen). Experience with vector databases (e.g., pgvector, Pinecone, Chroma, OpenSearch). Experience developing conversational AI systems / chatbots. Familiarity with deep learning frameworks (PyTorch, TensorFlow). Understanding of or experience with protocols and standards for connecting LLMs to external tools and databases, such as Model Context Protocol (MCP). Experience deploying models at scale in production environments. Additional information Compensation & Benefits Base salary starting at $105,000, commensurate with experience and qualifications Annual Discretionary Bonus Healthcare coverage that includes 3 medical plan options: Anthem – EPO HSA, EPO HSA MERP, and PPO; dental and vision through MetLife; and life insurance policies through Mutual of Omaha Flexible Spending Account (FSA) Paid Time Off Parental leave paid at 100% of base pay for all new parents 9 Company Holidays + 1 Floating Holiday 401(k) Plan - 4% matching and vested on day 1 College save-up plan & college loan repayment plan Monthly cell phone stipend Pre-tax account for Parking and Mass Transit Sia provides several wellness and incentive programs free of charge through the firm medical plan – Anthem - such as: Gym Reimbursement LiveHealth Online Well-being Coach Building Healthy Families Program And much more Diversity, Equity, Inclusion & Belonging At Sia, we believe in fostering a diverse, equitable and inclusive culture where our employees and partners are valued and thrive in a sense of belonging. We are committed to recruiting and developing a diverse network of employees and investing in their growth by providing unique opportunities for professional and cultural immersion. Our commitment toward inclusion motivates dynamic collaboration with our clients, building trust by creating an inclusive environment of curiosity and learning which affects lasting impact. Please visit our website for more information. Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs. Office Workplace Guidelines Sia is committed to providing a flexible workplace environment that supports client, business, and market needs. Consultants located in our primary market office locations—New York City, Charlotte, Seattle, and San Francisco—are expected to live within a reasonable commuting distance and attend the office at least three days per week. For Consultants outside of our primary markets, we can offer more flexible in-person requirements in accordance with your location. Work Authorization & Sponsorship At this time, Sia does not intend to pursue employment with applicants who will require now or in the future visa by our company for work authorization in the United States (i.e., H1-B visa, F-1 visa (OPT), TN visa, or any other non-immigrant status). Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs. I'm interested

At EY, we're all in to shape your future with confidence. We'll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. The O pportunity Supports the delivery of solution or infrastructure development services for AI/ML initiatives, applying strong technical capability and hands-on engineering experience. Contributes to the design, development, delivery, and maintenance of AI-enabled solutions or infrastructure while aligning to relevant engineering standards and project delivery expectations. Contributes to the integration of AI/ML capabilities into broader enterprise solutions, with a focus on quality, scalability, and user impact. Your Key Responsibilities Develop, test, and deploy, production-grade AI/ML, generative AI, and intelligent automation solutions. Support building and integrating LLM, RAG, and agentic solution components into enterprise applications and platforms. Support project delivery through disciplined execution, documentation, and status communication. Use modern AI-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms as part of day-to-day engineering delivery to improve delivery speed, code quality and engineering efficiency. AI and Engineering Skills: Gen AI Foundational: Demonstrated experience building retrieval-augmented systems that ground model outputs in enterprise knowledge sources, including chunking strategies, embedding pipelines, and retrieval optimization (e.g. LlamaIndex, LangChain, Pinecone, Weaviate, Azure AI Search, pgvector etc). Knowledge of embedding models, vector search, and semantic retrieval patterns used to ground LLM outputs in enterprise knowledge sources (e.g. OpenAI Embeddings, Azure AI Search, pgvector etc). Experience in prompt engineering techniques including zero-shot, few-shot, chain-of-thought, and structured output design, with the ability to systematically evaluate and iterate on prompt performance (e.g. DSPy, PromptFlow etc). Agentic and LLM Ops: Experience designing and building agentic systems including multi-agent orchestration patterns, tool use, and memory design across single and multi-step workflows (e.g. LangGraph, AutoGen, CrewAI, Semantic Kernel, NVIDIA NIM etc) Software Engineering: Hands-on software engineering proficiency in Python, with the ability to write clean, modular, production-quality code for LLM pipelines and agentic applications. Experience working with structured and unstructured data sets to support LLM application development, including data curation, preparation, and quality validation for model inputs. Familiarity with RESTful and event-driven API patterns including asynchronous workflows, service boundaries, and integration of enterprise data sources to expose LLM and agentic capabilities. Familiarity with containerization and orchestration concepts for packaging and deploying LLM applications in cloud environments (e.g. Docker, Kubernetes, Azure Container Apps, AWS ECS etc). Understanding of software engineering best practices as applied to ML systems, including modular code design, testing patterns for AI pipelines, and data quality validation. Soft Skills and additional attributes for success: Able to operate effectively as requirements, regulations, and technologies evolve. Collaborative and cross‑functional, working closely with engineering teams. Bachelor's degree preferred. 1+ years of applied engineering experience, including meaningful experience in AI/ML engineering roles. Demonstrated experience in the following areas will be a huge plus: Ability to understand business challenges and translate them into value-add AI solutions leveraging large language models and intelligent automation. Experience designing, building, and maintaining production-grade LLM applications, including end-to-end pipelines from data ingestion through model output delivery (e.g. Azure OpenAI, AWS Bedrock, Google Vertex AI etc). Familiarity with CI/CD and operationalization practices for AI systems, including model and workflow deployment, versioning, environment promotion, and release support in cloud or containerized environments. Familiarity with LLM fine-tuning approaches including instruction tuning and preference optimization, with an understanding of when fine-tuning is appropriate versus prompt-based solutions (e.g. LoRA, QLoRA, PEFT, NeMo Framework etc). Familiarity with responsible AI principles including bias and fairness evaluation, human-in-the-loop design, and explainability approaches in the financial services contexts. Familiarity with data pipeline design for AI workloads including ingestion, transformation, and quality validation. Familiarity with cloud-based platforms for building, training, and deploying scalable LLM solutions (e.g. Azure ML, AWS SageMaker, Google Vertex AI etc). What We Offer You At EY, we harness our collective strength to empower you to shape your future with confidence through professional growth, personal fulfillment and an inclusive culture. Learn more at ey.com/us/careers. We offer a comprehensive compensation and benefits package where you'll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job is: New York City, Boston, and Washington DC Metro Areas, Washington State, and Southern California offices – $56,000 to $103,800 Bay Area California offices – $58,300 to $108,100 All other offices locations in the US, including Sacramento – $46,700 to $95,200 Individual salaries within these ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options. Under our flexible vacation policy, you'll decide how much vacation time you need based on your own personal circumstances. You'll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being. Are you ready to shape your future with confidence? Apply today. To make the most of your application experience, please limit yourself to two applications within a six-month period. EY accepts applications for this position on an on-going basis. For those living in California, please click here for additional information. At EY, our values set the foundation for how we work and the behaviors we expect of our people. Any misrepresentation or falsification of information or lack of integrity at any point in the recruiting process may result in withdrawal of your candidacy, revocation of an offer or immediate termination of employment. EY | Building a better working world EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets. Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow. EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories. All in to shape the future with confidence. EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law. EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1-800-EY-HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY's Talent Shared Services Team (TSS) or email the TSS at ssc.customersupport@ey.com.

Job Description We are Lennar Lennar is one of the nation's leading homebuilders, dedicated to making an impact and creating an extraordinary experience for their Homeowners, Communities, and Associates by building quality homes and providing exceptional customer service, giving back to the communities in which we work and live in, and fostering a culture of opportunity and growth for our Associates throughout their career. Lennar has been recognized as a Fortune 500® company and consistently ranked among the top homebuilders in the United States. A Career that Empowers You to Build Your Future The AI Agent Developer designs, builds, deploys, and maintains intelligent AI agents that transform how Lennar operates across our corporate functions. Embedded directly with the teams you serve, you will sit alongside Associates to learn their workflows firsthand, then translate what you see into production-grade agents that enhance productivity, streamline operations, and enable data-driven decision making. Working at the intersection of business strategy, AI engineering, and user experience, you will partner closely with product owners, engineers, and function leaders — and carry what you learn in the field back into Lennar's enterprise AI roadmap. Your Responsibilities On The Team Embed directly with corporate function teams: shadowing Associates, mapping how work actually gets done, and partnering with product owners, business analysts, and engineers to scope high-impact AI agent use cases. Serve as an AI advisor to function leaders: presenting recommendations, framing trade-offs and risks in business terms, and building the confidence that moves teams from curiosity to committed adoption. Design agent personas, roles, and workflows, including decision logic, triggers, escalation paths, and human-in-the-loop rules. Build and configure AI agents using LLMs, retrieval-augmented frameworks, orchestration tools, and relevant APIs, integrating them with internal systems, data sources, and external services. Develop robust prompt engineering strategies, tool invocations, memory and context management, and monitoring mechanisms so agents act effectively and reliably. Deliver working prototypes and live demos early and often, using rapid feedback from business partners to shape each agent well before it reaches production. Test, iterate, and validate agent behavior under real-world conditions through unit tests, scenario simulations, edge-case handling, and performance and safety monitoring. Deploy agents into production environments and establish observability through logs, metrics, and usage dashboards, along with versioning, drift detection, and lifecycle management. Maintain agents post-deployment by refining prompts, tuning models, and improving workflows, while championing adoption through Associate training, enablement materials, user guides, and change logs. Ensure governance, compliance, security, and ethical considerations are built into agent design, including data access, audit trails, output safety, and escalation protocols. Track and report the measures that prove value, hours returned, cycle time reduced, quality improved, and bring field learnings back to the enterprise as reusable patterns and best practices. Your Toolbox Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, Business Analytics, AI, or a related technical field; advanced degree preferred. 2–4+ years of experience in software engineering, AI/ML development, or automation, with at least one year working with LLMs and conversational AI. Consultative mindset with genuine curiosity about how the business actually works, and the patience to sit with Associates until the real problem surfaces. Excellent communication and presentation skills, with the ability to demo working solutions to non-technical audiences and translate complex business requirements into functional AI workflows. Strong programming proficiency in Python and/or JavaScript/TypeScript, with demonstrated ability to build production-ready applications. Proficient SQL skills for querying, transforming, and joining complex enterprise datasets across multiple systems. Working understanding of AI agent concepts including LLMs, retrieval-augmented generation (RAG), prompt engineering best practices, and agent orchestration frameworks; familiarity with LangChain, LlamaIndex, function-calling patterns, Model Context Protocol (MCP), or multi-agent architectures is a plus. Experience with cloud platforms, particularly AWS services such as Lambda, S3, and Bedrock, plus hands-on experience with Claude, Codex, GitHub, and Spotify Backstage; agentic coding tools such as Claude Code or Codex preferred. Experience designing for production environments including scalability, reliability, monitoring, error handling, and performance optimization; MLOps practices such as model monitoring, drift detection, and A/B testing are a plus. Understanding of responsible AI principles and hands-on experience implementing safety guardrails, with the discretion to handle sensitive Associate and business information; familiarity with enterprise systems such as Workday, ServiceNow, and Docebo preferred. Physical & Office/Site Presence Requirements: This is primarily a sedentary office position which requires the incumbent to have the ability to operate computer equipment, speak, hear, bend, stoop, reach, lift, and move and carry up to 25 lbs. Finger dexterity is necessary. This description outlines the basic responsibilities and requirements for the position noted. This is not a comprehensive listing of all job duties of the Associates. Duties, responsibilities, and activities may change at any time with or without notice. Lennar is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. Life at Lennar At Lennar, we are committed to fostering a supportive and enriching environment for our Associates, offering a comprehensive array of benefits designed to enhance their well-being and professional growth. Our Associates have access to robust health insurance plans, including Medical, Dental, and Vision coverage, ensuring their health needs are well taken care of. Our 401(k) Retirement Plan, complete with a $1 for $1 Company Match up to 5%, helps secure their financial future, while Paid Parental Leave and an Associate Assistance Plan provide essential support during life's critical moments. To further support our Associates, we provide an Education Assistance Program and up to $30,000 in Adoption Assistance, underscoring our commitment to their diverse needs and aspirations. From the moment of hire, they can enjoy up to three weeks of vacation annually, alongside generous Holiday, Sick Leave, and Personal Day policies. Additionally, we offer a New Hire Referral Bonus Program, significant Home Purchase Discounts, and unique opportunities such as the Everyone's Included Day. At Lennar, we believe in investing in our Associates, empowering them to thrive both personally and professionally. Lennar Associates will have access to these benefits as outlined by Lennar's policies and applicable plan terms. Visit Lennartotalrewards.com to view our suite of benefits. Join the fun and follow us on social media to see what's happening at our company, and don't forget to connect with us on Lennar: Overview | LinkedIn for the latest job opportunities. Lennar is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. Apply