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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 Senior AI/ML Platform Engineer will design, implement, operate, and continuously improve the state's shared enterprise AI/ML platform. The role focuses on platform automation, MLOps, CI/CD, cloud infrastructure, observability, reliability engineering, and cost optimization. The engineer will own critical platform components end-to-end, improve developer productivity and deployment efficiency, establish engineering standards, and mentor other engineers while enabling secure, reliable, and scalable AI/ML services across multiple state agencies. Work Location: This role is expected to be onsite at Linthicum, MD. Some teleworking may be permitted based on assignment and team needs. Interview: An in-person interview may be required. Key Responsibilities Pipeline Automation and CI/CD: Design and maintain enterprise-grade ML CI/CD pipelines for automated model validation, testing, A/B testing, canary deployments, rollback, and production releases. Implement GitOps practices and improve model-to-production delivery times. Observability and Reliability: Establish monitoring, alerting, and service-level objectives (SLOs) for platform services and deployed models. Implement model monitoring for data drift, concept drift, and performance degradation. Lead incident response, root-cause analysis, and corrective actions. Cloud Cost Optimization: Monitor cloud expenditure, identify resource inefficiencies, and optimize GPU/CPU utilization, autoscaling, workload scheduling, and infrastructure sizing. Evaluate platform components based on performance and total cost of ownership. Developer Experience and Self-Service: Develop internal SDKs, CLI tools, reusable templates, APIs, and standardized deployment workflows. Maintain API versioning, backward compatibility, documentation, runbooks, architecture decision records (ADRs), and onboarding guides. Platform Engineering and Operations: Own the architecture, implementation, maintenance, and production support of core platform components. Ensure scalability, availability, security, maintainability, and operational efficiency. Technical Leadership and Mentorship: Conduct code and design reviews, mentor engineers, establish engineering best practices, participate in architecture reviews and technical evaluations, and collaborate with agency teams and platform leadership. Required Qualifications Bachelor's degree from an accredited college or university in Computer Science, Information Systems, Engineering, Business, or another related scientific or technical discipline. A master's degree is preferred. At least ten (10) years of experience in platform engineering, MLOps, or data/software engineering, including at least two (2) years working with production ML systems. Strong proficiency in Python and experience developing and maintaining reliable, production-grade software. Demonstrated experience designing and improving ML CI/CD pipelines, automation frameworks, and deployment processes. Experience with AWS, Azure, or Google Cloud Platform, including cloud resource management and cost optimization. Advanced experience with Kubernetes, including cluster management, resource quotas, autoscaling, and workload scheduling. Hands-on experience with observability tools such as Prometheus, Grafana, Datadog, and OpenTelemetry, or equivalent technologies. Production-scale experience with Terraform and Ansible for infrastructure automation and configuration management. Strong understanding of the ML lifecycle, including experiment tracking, model registries, feature stores, model serving, and drift monitoring. Preferred Qualifications Experience in state government, the public sector, or highly regulated environments. Familiarity with FedRAMP, NIST AI Risk Management Framework (AI RMF), and applicable cybersecurity and privacy requirements, including CJIS, HIPAA, IRS Publication 1075, and PII protection, where applicable. Experience with responsible AI, model explainability, fairness auditing, bias mitigation, and model risk management. Knowledge of platform observability, reliability engineering, incident management, disaster recovery, and cloud cost optimization. Strong technical leadership, mentoring, documentation, communication, and cross-functional collaboration skills, including the ability to explain complex technical concepts to executive and nontechnical stakeholders. Skills And Competencies Systems ownership: Takes end-to-end responsibility for platform reliability and operational outcomes. Quantitative problem-solving: Measures improvements in latency, cloud cost, mean time to recovery (MTTR), and developer onboarding time. Technical communication: Produces clear architecture documents, technical specifications, runbooks, and incident reports. Cross-functional collaboration: Communicates technical trade-offs effectively with engineers, agency stakeholders, and leadership. Mentorship: Supports engineering development through code reviews, knowledge sharing, and collaborative problem-solving. 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 . 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. Apply Now

Overview It is an exciting time to join Fisher Investments as we expand our Machine Learning capabilities to support our international growth. We are looking for a Senior MLOps Engineer to build the infrastructure, pipelines, and operational practices that bring machine learning and generative AI solutions from experimentation into reliable, secure, and scalable production services. The Opportunity: As a Senior MLOps Engineer, you will build, deploy, and operate machine learning and generative AI solutions in enterprise environments. Working closely with AI leads, ML engineers, and platform teams, you will turn experimental workflows into production-ready services and establish the operational practices needed to support them. You will report to the AI Team Leader, Technology Innovation. The Day-to-Day: Design, build, and operate end-to-end data and ML pipelines on Azure and Databricks, including ingestion, validation, feature engineering, model training, fine-tuning, evaluation, and deployment Build and maintain CI/CD workflows for application code, model artifacts, prompt templates, retrieval and context configurations, evaluation datasets, and infrastructure Operationalize retrieval-augmented generation (RAG) and context engineering workflows, including document ingestion, chunking, embedding generation, indexing, retrieval, context selection and assembly, and evaluation of retrieval and response quality Collaborate with ML engineers, data engineers, and the AI Lead to translate experimental training, fine-tuning, evaluation, and inference workflows into reproducible, production-ready systems Develop and maintain production-grade APIs and microservices using Python and FastAPI to expose model inference and integrate AI capabilities into enterprise applications Deploy, operate, and scale model-serving endpoints and containerized inference services using Azure-managed services, Docker, and Kubernetes to workload requirements Provision and manage cloud and platform resources through infrastructure as code, ensuring consistent, repeatable, and auditable environment configurations Implement automated testing and release quality gates for application code, data quality, model performance, retrieval quality, AI safety, and deployment readiness Establish model and artifact versioning, experiment tracking, and deployment traceability to support reproducibility and troubleshooting Monitor and improve model quality, data and retrieval pipeline reliability, inference latency, throughput, service availability, and infrastructure cost efficiency Partner with infrastructure, security, and governance teams to implement appropriate access controls, auditability, and operational safeguards Your Qualifications: 10+ years of experience building data-driven software solutions, including 5+ years of hands-on machine learning engineering experience 8+ years of Python development experience, including building and maintaining production services, APIs, or data pipelines Demonstrated experience deploying and operating machine learning or generative AI solutions in production Experience with Databricks, including Delta Lake, Auto Loader, Workflows, Feature Engineering, and deployment automation using Databricks Asset Bundles Hands-on experience building CI/CD pipelines and implementing automated testing and deployment workflows for data and ML systems Hands-on experience with cloud-native AI infrastructure and related technologies, including Azure Foundry, Azure Kubernetes Service, Docker, and GPU-backed workloads Hands-on experience integrating and operating graph databases to support knowledge graphs, relationship-based retrieval, or generative AI applications Bachelor's degree in computer science, Artificial Intelligence, or a related field, or equivalent professional experience Experience deploying models for low-latency, high-throughput inference using frameworks such as vLLM and other GPU-based model-serving toolsExperience with distributed training, GPU optimization, and GPU computing technologies such as CUDA and RAPIDS Why Fisher Investments: We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like: 100% paid medical, dental and vision premiums for you and your qualifying dependents A 50% 401(k) match, up to the IRS maximum 20 days of PTO, plus 10 paid holidays Family Support programs including 8 week Paid Primary Caregiver Leave, $10,000 fertility, family forming, and hormonal health assistance, and back-up child, adult, and elder care This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change. FISHER INVESTMENTS IS AN EQUAL OPPORTUNITY EMPLOYER

About The Role The role owns the infrastructure that turns machine learning models into reliable, production systems. This means designing CI/CD pipelines for ML, automating training and deployment workflows, and building the observability layer that catches model drift before it impacts users. The team supports data scientists and ML engineers across the organization - anyone shipping a model depends on the platforms and tooling this role builds. The work spans Kubernetes-based serving infrastructure, experiment tracking, feature stores, and GPU resource orchestration. Key Responsibilities Design and maintain CI/CD pipelines for ML workflows using tools like GitHub Actions, Airflow, or Kubeflow, enabling safe automated model releases Build and operate model serving infrastructure on Kubernetes (KServe, Seldon, or Triton) supporting both real-time and batch inference at scale Implement monitoring and alerting for production models: data drift detection, latency SLAs, prediction quality dashboards using tools like Evidently or Prometheus Manage ML platform tooling - experiment tracking (MLflow, W&B), model registries, and feature stores - and drive adoption across ML teams Optimize GPU utilization and cloud costs across training and inference workloads (AWS, GCP, or Azure), including autoscaling and spot instance strategies Automate data pipeline orchestration and ensure reproducibility of training runs through versioned datasets, code, and configs Partner with ML engineers and data scientists to debug production issues, reduce deployment friction, and define platform roadmaps What We Are Looking For 3-6 years of experience in MLOps, DevOps, or ML engineering, with at least 2 years supporting ML systems in production Strong proficiency with Kubernetes, Docker, and infrastructure-as-code (Terraform) in a cloud environment (AWS, GCP, or Azure) Hands-on experience with ML workflow tools: MLflow, Kubeflow, Airflow, or Vertex AI Pipelines Solid Python and/or Go skills; comfort building internal tooling and APIs Experience with model serving frameworks and inference optimization (ONNX, Triton, vLLM, or TensorRT) BS in Computer Science, Engineering, or equivalent practical experience Bonus: Experience with LLM deployment at scale, distributed training infrastructure (Ray, Spark), or FinOps/GPU cost optimization; contributions to open-source MLOps tooling

Overview It is an exciting time to join Fisher Investments as we expand our Machine Learning capabilities to support our international growth. We are looking for a Senior MLOps Engineer to build the infrastructure, pipelines, and operational practices that bring machine learning and generative AI solutions from experimentation into reliable, secure, and scalable production services. The Opportunity: As a Senior MLOps Engineer, you will build, deploy, and operate machine learning and generative AI solutions in enterprise environments. Working closely with AI leads, ML engineers, and platform teams, you will turn experimental workflows into production-ready services and establish the operational practices needed to support them. You will report to the AI Team Leader, Technology Innovation. The Day-to-Day: Design, build, and operate end-to-end data and ML pipelines on Azure and Databricks, including ingestion, validation, feature engineering, model training, fine-tuning, evaluation, and deployment Build and maintain CI/CD workflows for application code, model artifacts, prompt templates, retrieval and context configurations, evaluation datasets, and infrastructure Operationalize retrieval-augmented generation (RAG) and context engineering workflows, including document ingestion, chunking, embedding generation, indexing, retrieval, context selection and assembly, and evaluation of retrieval and response quality Collaborate with ML engineers, data engineers, and the AI Lead to translate experimental training, fine-tuning, evaluation, and inference workflows into reproducible, production-ready systems Develop and maintain production-grade APIs and microservices using Python and FastAPI to expose model inference and integrate AI capabilities into enterprise applications Deploy, operate, and scale model-serving endpoints and containerized inference services using Azure-managed services, Docker, and Kubernetes to workload requirements Provision and manage cloud and platform resources through infrastructure as code, ensuring consistent, repeatable, and auditable environment configurations Implement automated testing and release quality gates for application code, data quality, model performance, retrieval quality, AI safety, and deployment readiness Establish model and artifact versioning, experiment tracking, and deployment traceability to support reproducibility and troubleshooting Monitor and improve model quality, data and retrieval pipeline reliability, inference latency, throughput, service availability, and infrastructure cost efficiency Partner with infrastructure, security, and governance teams to implement appropriate access controls, auditability, and operational safeguards Your Qualifications: 10+ years of experience building data-driven software solutions, including 5+ years of hands-on machine learning engineering experience 8+ years of Python development experience, including building and maintaining production services, APIs, or data pipelines Demonstrated experience deploying and operating machine learning or generative AI solutions in production Experience with Databricks, including Delta Lake, Auto Loader, Workflows, Feature Engineering, and deployment automation using Databricks Asset Bundles Hands-on experience building CI/CD pipelines and implementing automated testing and deployment workflows for data and ML systems Hands-on experience with cloud-native AI infrastructure and related technologies, including Azure Foundry, Azure Kubernetes Service, Docker, and GPU-backed workloads Hands-on experience integrating and operating graph databases to support knowledge graphs, relationship-based retrieval, or generative AI applications Bachelor's degree in computer science, Artificial Intelligence, or a related field, or equivalent professional experience Experience deploying models for low-latency, high-throughput inference using frameworks such as vLLM and other GPU-based model-serving toolsExperience with distributed training, GPU optimization, and GPU computing technologies such as CUDA and RAPIDS Compensation: $130,000 - $185,000 base salary per year in the state of WA. New hires should expect to start at the lower end of the range depending on experience Eligible for a discretionary bonus based on firm and individual performance Why Fisher Investments: We work for a bigger purpose: bettering the investment universe. We take great pride in our inclusive culture, our learning and development framework customized for every employee, and our Great Place to Work Certification. It's the people that make the Fisher purpose possible, and we invest in them by offering exceptional benefits like: 100% paid medical, dental and vision premiums for you and your qualifying dependents A 50% 401(k) match, up to the IRS maximum 20 days of PTO, plus 10 paid holidays Family Support programs including 8 week Paid Primary Caregiver Leave, $10,000 fertility, family forming, and hormonal health assistance, and back-up child, adult, and elder care This is an in-office role. Based on your role, tenure, and performance eligibility you may have the opportunity to participate in our hybrid work from home program. This program is subject to change. FISHER INVESTMENTS IS AN EQUAL OPPORTUNITY EMPLOYER

Job Title: AI Prompt Engineer Experience: 10+ Years Location: Whippany , NJ ( 3 days onsite) Full Time Salary OPT EAD Also can Apply Role Overview We are seeking an innovative and detail-oriented AI Prompt Engineer to design, optimize, and evaluate prompts for Generative AI and Large Language Model (LLM) applications. The ideal candidate will work closely with business stakeholders, data scientists, AI engineers, and product teams to develop high-quality prompts that improve AI accuracy, reliability, and user experience across enterprise use cases. Key Responsibilities Design, develop, test, and refine prompts for LLMs such as GPT /Claude . Create prompt engineering best practices for enterprise applications. Evaluate model outputs and continuously optimize prompts for accuracy, relevance, safety, and performance. Develop prompt libraries, prompt chains, and AI agent workflows. Work with RAG (Retrieval Augmented Generation), vector databases, and knowledge management systems to improve contextual responses. Monitor AI application performance and implement prompt improvements based on user feedback. Document prompt engineering methodologies, testing results, and governance standards. Required Skills Strong understanding of Generative AI, LLMs, and Prompt Engineering concepts. Hands-on experience with OpenAI/ Anthropic Claude or similar AI platforms. Knowledge of prompt patterns such as: Zero-shot Few-shot Chain-of-Thought (CoT) Tree-of-Thought (ToT) ReAct Role-based prompting Experience with Python and AI development frameworks. Familiarity with any of the frameworks like LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent frameworks. Understanding of RAG architectures and vector databases such as Pinecone etc Strong analytical and problem-solving skills. Excellent communication and documentation abilities.

Build your big career with the firm that does Big Law, Better . McDermott Will & Schulte is a leading global law firm that brings together more than 1,700+ lawyers and 1,400 business professionals. We celebrate excellence, collaboration, and community and have been recognized as a top workplace by USA Today, Fortune , The American Lawyer, Vault and others. We are also certified by Great Place to Work. At McDermott, we don't just invest in your future, we accelerate your career – wherever it may lead. That includes supporting you both in and outside of the office. With Us, You'll Find A firm where everyone belongs: Our award-winning culture prioritizes warmth and authenticity — we encourage you to be yourself! Enthusiasm for all perspectives: We're smarter and stronger when everyone has a voice and a seat at the table. We welcome unique viewpoints and ideas, and we make opportunities for you and your career to thrive. Support to feel your best and do your best: Wellness is integral to building a successful career and a rich life. That's why our benefits program supports your physical, emotional, mental, and financial health, with an emphasis on work-life balance. Real rewards for real work: We offer generous compensation packages that recognize hard work and excellence. Job Description Position Summary: Are you a Product Manager, Pre-Sales Engineer, or Business Analyst who lives and breathes AI? This role was built for you. McDermott, Will & Schulte LLP is among the most ambitious firms in the world when it comes to AI — and we're just getting started. We're building a purpose-built AI capability inside the firm: intelligent applications, agentic workflows, and deep integrations that are transforming how our attorneys and staff professionals work and how we serve clients. The Applied AI Solutions Analyst sits at the center of that effort. You won't hand requirements to a dev team. You'll discover opportunities, design solutions, and build them yourself using the most advanced AI tools available. What You'll Do Partner with attorneys and operational leaders to surface workflow pain points and translate them into AI use cases Design and build AI solutions using state-of-the-art AI foundation platforms — from conversational tools to full document automation Orchestrate agentic workflows that autonomously handle multi-step legal and business processes via integration frameworks and APIs Use prompting to engineer bespoke applications and solutions with high-quality output Integrate AI solutions with M365 applications, Enterprise Data Warehouse, the firm's Document Management System and other enterprise applications Run iterative feedback loops with stakeholders; validate solution designs and maintain clear documentation throughout delivery Monitor the LLM landscape and bring actionable AI innovations back to the team TECHNOLOGIES Leading AI Foundation Models Agentic Frameworks Vector Store(s) Enterprise Data Platform Agentic Workflows MCP REST APIs Azure iManage SharePoint Microsoft 365 Git What You Bring 3+ years delivering software-based solutions in a Business Analyst, solutions, or technical product role Demonstrated AI building experience — delivered AI tools, workflows, or agents professionally or personally Deep prompt engineering skill; proficiency with at least one leading AI foundation platform API integration experience — REST, JSON, and cloud environments (Azure preferred) Excellent communication skills — can explain model behavior to a managing partner BS in Computer Science, Information Systems, Engineering, or equivalent experience Preferred Experience with agentic frameworks, tool-use chains, or autonomous agent orchestration Hands-on experience with AI-assisted development tools and agentic workflow frameworks Exposure to legal industry workflows — matter management, KM, or client delivery Familiarity with responsible AI and data governance in regulated environments WHY MWS Executive-sponsored AI program — one of the most ambitious in global law Build with frontier AI tools on problems that genuinely matter Remote-first, collaborative team that ships fast and celebrates impact Career-defining work at the frontier of legal AI transformation Successful candidates will be provided with outstanding career opportunities and will receive a competitive total rewards package with the opportunity to earn performance-based bonuses. Target Hiring Range $93,000 - $169,000 Please note that quoted salary ranges are not guarantees of what final salary offers may be. Base pay is based on market location and may vary depending on job-related knowledge, skills, experience, and geographic location. Base pay is only one part of the Total Rewards that MWS provides to compensate and recognize our staff professionals for their work. Full time positions are eligible for a discretionary bonus and a comprehensive benefits package. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Physical Demands And Work Environment The physical demands and work environment characteristics described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Physical Demands While performing the duties of this job, the employee is required to sit, use hands, reach with hands and arms, stoop, talk and hear Employee must occasionally lift up to twenty (20) pounds Work Environment Typical indoor office environment Disclaimer The above statements are intended to describe the general nature and level of the work being performed by people within this classification. They are not intended to be an exhaustive list of all responsibilities, duties and skills required of employees assigned to this job.

AI Prompt Designer – Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title: AI Prompt Designer Location: 1 00% Remote (U.S.) Position Type: Full-time, Direct W2 Salary Range: $100,000 Annually Experience Required: 6+ years Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position. Job Summary We are looking for a AI Prompt Designer to define and lead the strategy, patterns, and tooling for designing prompts, agentic workflows, and LLM-based application architectures across the organization. The role combines deep practical mastery of modern LLMs with the discipline of building reusable design patterns, evaluation frameworks, and developer tooling that scale across many teams and use cases. The ideal candidate has shipped LLM-powered products in production, is fluent in prompt design patterns and agent architectures, and brings strong judgment about when and how to apply different LLM techniques responsibly. Required Qualifications Bachelor's or Master's degree in Computer Science, Computational Linguistics, or a related field Six or more years of software engineering experience, with significant time on LLM-based applications Demonstrated experience shipping LLM-powered products to production Deep familiarity with modern LLM APIs and agent frameworks Strong understanding of retrieval-augmented generation, embeddings, and vector databases Experience designing evaluation pipelines for non-deterministic systems Strong Python skills and comfort with modern application frameworks Solid grasp of responsible AI principles, including safety and policy considerations Excellent written and verbal communication skills Track record of mentoring engineers and influencing technical direction Preferred Qualifications Public writing, talks, or open-source contributions on LLM application development Experience with multi-agent architectures and complex tool-use systems Familiarity with fine-tuning workflows and when to choose them over prompting Exposure to product domains such as customer support, coding assistants, or analytics agents Experience integrating LLMs into enterprise software systems with strict compliance requirements How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to hilda@bvteck.com Bright Vision Technologies is an Equal Opportunity Employer. Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment. Powered by JazzHR vqg5VBnuZ3

Build your big career with the firm that does Big Law, Better . McDermott Will & Schulte is a leading global law firm that brings together more than 1,700+ lawyers and 1,400 business professionals. We celebrate excellence, collaboration, and community and have been recognized as a top workplace by USA Today, Fortune , The American Lawyer, Vault and others. We are also certified by Great Place to Work. At McDermott, we don't just invest in your future, we accelerate your career – wherever it may lead. That includes supporting you both in and outside of the office. With Us, You'll Find A firm where everyone belongs: Our award-winning culture prioritizes warmth and authenticity — we encourage you to be yourself! Enthusiasm for all perspectives: We're smarter and stronger when everyone has a voice and a seat at the table. We welcome unique viewpoints and ideas, and we make opportunities for you and your career to thrive. Support to feel your best and do your best: Wellness is integral to building a successful career and a rich life. That's why our benefits program supports your physical, emotional, mental, and financial health, with an emphasis on work-life balance. Real rewards for real work: We offer generous compensation packages that recognize hard work and excellence. Job Description Position Summary: Are you a Product Manager, Pre-Sales Engineer, or Business Analyst who lives and breathes AI? This role was built for you. McDermott, Will & Schulte LLP is among the most ambitious firms in the world when it comes to AI — and we're just getting started. We're building a purpose-built AI capability inside the firm: intelligent applications, agentic workflows, and deep integrations that are transforming how our attorneys and staff professionals work and how we serve clients. The Applied AI Solutions Analyst sits at the center of that effort. You won't hand requirements to a dev team. You'll discover opportunities, design solutions, and build them yourself using the most advanced AI tools available. What You'll Do Partner with attorneys and operational leaders to surface workflow pain points and translate them into AI use cases Design and build AI solutions using state-of-the-art AI foundation platforms — from conversational tools to full document automation Orchestrate agentic workflows that autonomously handle multi-step legal and business processes via integration frameworks and APIs Use prompting to engineer bespoke applications and solutions with high-quality output Integrate AI solutions with M365 applications, Enterprise Data Warehouse, the firm's Document Management System and other enterprise applications Run iterative feedback loops with stakeholders; validate solution designs and maintain clear documentation throughout delivery Monitor the LLM landscape and bring actionable AI innovations back to the team TECHNOLOGIES Leading AI Foundation Models Agentic Frameworks Vector Store(s) Enterprise Data Platform Agentic Workflows MCP REST APIs Azure iManage SharePoint Microsoft 365 Git What You Bring 3+ years delivering software-based solutions in a Business Analyst, solutions, or technical product role Demonstrated AI building experience — delivered AI tools, workflows, or agents professionally or personally Deep prompt engineering skill; proficiency with at least one leading AI foundation platform API integration experience — REST, JSON, and cloud environments (Azure preferred) Excellent communication skills — can explain model behavior to a managing partner BS in Computer Science, Information Systems, Engineering, or equivalent experience Preferred Experience with agentic frameworks, tool-use chains, or autonomous agent orchestration Hands-on experience with AI-assisted development tools and agentic workflow frameworks Exposure to legal industry workflows — matter management, KM, or client delivery Familiarity with responsible AI and data governance in regulated environments WHY MWS Executive-sponsored AI program — one of the most ambitious in global law Build with frontier AI tools on problems that genuinely matter Remote-first, collaborative team that ships fast and celebrates impact Career-defining work at the frontier of legal AI transformation Successful candidates will be provided with outstanding career opportunities and will receive a competitive total rewards package with the opportunity to earn performance-based bonuses. Target Hiring Range $93,000 - $169,000 Please note that quoted salary ranges are not guarantees of what final salary offers may be. Base pay is based on market location and may vary depending on job-related knowledge, skills, experience, and geographic location. Base pay is only one part of the Total Rewards that MWS provides to compensate and recognize our staff professionals for their work. Full time positions are eligible for a discretionary bonus and a comprehensive benefits package. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. Physical Demands And Work Environment The physical demands and work environment characteristics described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Physical Demands While performing the duties of this job, the employee is required to sit, use hands, reach with hands and arms, stoop, talk and hear Employee must occasionally lift up to twenty (20) pounds Work Environment Typical indoor office environment Disclaimer The above statements are intended to describe the general nature and level of the work being performed by people within this classification. They are not intended to be an exhaustive list of all responsibilities, duties and skills required of employees assigned to this job.

About Persistent We are an AI-led, platform-driven Digital Engineering and Enterprise Modernization partner, combining deep technical expertise and industry experience to help our clients anticipate what's next. Our offerings and proven solutions create a unique competitive advantage for our clients by giving them the power to see beyond and rise above. We work with many industry-leading organizations across the world, including 20 Fortune 50 companies and 4 of the 5 top banks in both the US and India, and numerous innovators across the healthcare ecosystem. Our disruptor's mindset, commitment to client success, and agility to thrive in the dynamic environment have enabled us to sustain our growth momentum. Persistent has been recognized across top industry platforms for innovation, leadership, and inclusion. We reported $1,654.4M FY26 revenue with 17.4% Y-o-Y growth. We have delivered 24 sequential quarters of growth with $436.0M in Q4 FY26 revenue, up 3.2% Q-o-Q and 16.2% Y-o-Y growth. Our 27,500+ global team members, located in 18 countries, have been instrumental in helping the market leaders transform their industries. We have been recognized as the Fastest Growing IT Services Brand Globally in the 2026 Brand Finance IT Services 25 Report. We named a Leader in the Everest Group Private Equity (PE) Services PEAK Matrix® Assessment 2026 and Software Product Engineering PEAK Matrix® Assessment 2026. About Position Role: AI Prompt Engineer Location: Whippany, NJ (3 days onsite) Experience: 8+ Years Job Type: Full Time Mandatory Skills: AI, Prompt Engineer, GPT /Claude, LLM, RAG Expertise You'll Bring Strong understanding of Generative AI, LLMs, and Prompt Engineering concepts. Hands-on experience with OpenAI/ Anthropic Claude or similar AI platforms. Knowledge of prompt patterns such as: Zero-shot Few-shot Chain-of-Thought (CoT) Tree-of-Thought (ToT) ReAct Role-based prompting Experience with Python and AI development frameworks. Familiarity with any of the frameworks like LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent frameworks. Understanding of RAG architectures and vector databases such as Pinecone etc Strong analytical and problem-solving skills. What You'll Do Design, develop, test, and refine prompts for LLMs such as GPT /Claude . Create prompt engineering best practices for enterprise applications. Evaluate model outputs and continuously optimize prompts for accuracy, relevance, safety, and performance. Develop prompt libraries, prompt chains, and AI agent workflows. Work with RAG (Retrieval Augmented Generation), vector databases, and knowledge management systems to improve contextual responses. Monitor AI application performance and implement prompt improvements based on user feedback. Document prompt engineering methodologies, testing results, and governance standards. Benefits Competitive salary and benefits package Culture focused on talent development with quarterly promotion cycles and company-sponsored higher education and certifications Opportunity to work with cutting-edge technologies Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards Annual health check-ups Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents Values-Driven, People-Centric & Inclusive Work Environment: Persistent is dedicated to fostering diversity and inclusion in the workplace. We invite applications from all qualified individuals, including those with disabilities, and regardless of gender or gender preference. We welcome diverse candidates from all backgrounds. We support hybrid work and flexible hours to fit diverse lifestyles. Our office is accessibility-friendly, with ergonomic setups and assistive technologies to support employees with physical disabilities. If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment Let's unleash your full potential at Persistent persistent.com/careers For more details, please contact Siva Sankavi Murugan (Siva_murugan@persistent.com) "Persistent is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind."

Bounteous AI Engineer / AI Search Specialist rather than a traditional AWS or Amazon Connect engineer. We are looking for someone with demonstrated experience building and optimizing production-grade AI search and conversational experiences. GenAI & AI Search Hands-on experience with: Retrieval-Augmented Generation (RAG) architectures LLM integration and orchestration Prompt engineering, guardrails, and response grounding Context management and conversational memory Vector search and knowledge retrieval LLM evaluation frameworks, testing, and performance tuning Developing AI responses grounded in enterprise content and knowledge sources Preferred AI Search Domain Experience AI-powered search and question-answering solutions Conversational and chat-based search experiences Citation and source attribution patterns Relevance tuning and retrieval optimization Search analytics, user feedback loops, and answer-quality improvements Healthcare Experience (Preferred) Familiarity with healthcare payer and member-facing experiences Understanding of benefits, claims, eligibility, provider, and healthcare content Experience working in HIPAA-regulated environments Knowledge of responsible AI practices for healthcare use cases Bonus Points Strong AWS Development Experience, Including Amazon Connect Customer Profiles AWS Bedrock AWS Lambda Amazon CloudWatch Amazon Connect Contact Lens Amazon S3 DynamoDB AWS architecture, security, and operational best practices Ideal Candidate We're looking for an engineer who has recently delivered AI-powered search, retrieval, chatbot, or copilot-style solutions in production environments and can contribute immediately to our AI Search roadmap. AWS experience remains valuable, but expertise in GenAI, retrieval, relevance optimization, and LLM evaluation is the primary need for this role.

Job Title: AI Engineer Location: Coppell, Texas Work Arrangement: On-Site Job Summary We are seeking an AI Engineer to join our engineering team and support AI-driven transformation initiatives within the pharmaceutical industry. The ideal candidate will design, develop, and test AI-powered solutions using AWS AI services, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and advanced prompt engineering techniques. The AI Engineer will work closely with product, design, and engineering teams to develop effective AI workflows, optimize prompts, and integrate local files, documents, and enterprise data sources into AI-powered applications. This role will also focus on AI performance evaluation, security testing, and governance to ensure reliable, secure, and responsible AI implementation. Key Responsibilities Develop and maintain a structured prompt library for common use cases, including summarization, question answering, information extraction, code generation, and file analysis. Apply advanced prompt engineering techniques, including few-shot prompting, role specification, and structured input formats, to improve AI output quality. Design and implement RAG pipelines that connect enterprise systems such as Warehouse Management Systems (WMS), Electronic Data Interchange (EDI) logs, Standard Operating Procedures (SOPs), contract repositories, and other data sources. Develop, deploy, and optimize LLM-powered applications, including internal knowledge assistants and client-facing chat solutions. Extend AI-powered response repositories and identify opportunities to automate workflows, improve processes, and enhance system performance. Conduct A/B testing of prompt variations, evaluate AI-generated responses, and document performance benchmarks. Work with Claude Desktop and similar AI tools to support document-based workflows involving PDFs, Word documents, spreadsheets, and code files. Create reusable prompt patterns for multi-file inputs, long-context documents, and structured data analysis. Develop and maintain internal tools for prompt evaluation, testing, and performance monitoring. Test AI workflows for vulnerabilities, potential bias, and misuse risks while supporting AI safety, security, and governance requirements. Collaborate with cross-functional teams and provide guidance to end users on effective AI tools and prompt usage. Required Skills Experience with Generative AI, Large Language Models (LLMs), and AI application development. Hands-on experience with AWS AI services and cloud-based AI solutions. Strong understanding of prompt engineering, prompt optimization, and LLM evaluation techniques. Experience designing and developing Retrieval-Augmented Generation (RAG) pipelines. Knowledge of enterprise data integration and document-processing workflows. Familiarity with Claude Desktop or similar AI tools for document analysis and file-based workflows. Experience with AI testing, performance benchmarking, and prompt evaluation. Understanding of AI security, data privacy, responsible AI practices, and governance. Strong analytical, problem-solving, communication, and collaboration skills. Preferred Qualifications Experience working with WMS, EDI systems, SOP repositories, contract data, or enterprise knowledge bases. Familiarity with Python, APIs, vector databases, embeddings, and document-processing frameworks. Experience developing AI assistants, enterprise chatbots, or automated document-analysis solutions. Exposure to pharmaceutical, biotechnology, life sciences, or other regulated industry environments.

About Entegrata Entegrata helps law firms turn data into decision-grade insights. Founded in 2023 and headquartered in Indianapolis, we are a venture-backed SaaS company delivering a turnkey, Azure-based data lakehouse platform purpose-built for law firms. Our platform consolidates siloed legal, operational, and financial data into a single governed source of truth, with AI-powered analytics on top. Our mission is to become the data platform of choice for the top law firms in the world. The Role Law firms are pouring both time and money into AI, and many efforts stall on the same point: the data underpinning the endeavor. Your job is to fix that. As our first Applied AI + NLP Engineer, you build the intelligence that turns Entegrata's governed data platform into an enablement layer for a firm's entire AI strategy. You will ship the conversational analytics that let firm leaders interact with their data in plain English and get governed, accurate answers they can act on. In addition to helping unlock value related to structured data from firm systems, you will help us turn legal documents into discrete data. This distillation of documents will help our customers tap into key content with added precision, enabling new insights for customers. You will also build the retrieval and natural-language-to-query capabilities that help firms get more out of the AI tools they already run, and light up new business and practice of law use cases that were not possible before. Because this all sits on top of our governed semantic layer, accuracy, trust, and respect for firm data boundaries are non-negotiable. This is a senior, hands-on role at the core of our AI enablement roadmap. You will need to be comfortable working where data engineering, AI and product intersect. Key Responsibilities AI for Structured Data & Analytics (Initial Focus) Build systems that translate natural language questions into deterministic analytical workflows and governed queries over structured legal data. Help define and implement semantic models and metadata that enable LLMs and AI agents to reason consistently over operational, business, and legal data. Improve intent recognition, query planning, and ambiguity resolution to deliver accurate, consistent responses—even when user requests are incomplete or underspecified. Develop AI agents capable of accessing, interpreting, and synthesizing structured data and legal content to answer complex business questions. Build orchestration logic that intelligently selects the appropriate data sources, models, tools, and workflows for each request. Implement guardrails that ensure AI responses align with governed business definitions, semantic models, and firm conventions. Build evaluation frameworks, benchmark datasets, and automated tests that continuously improve answer quality, determinism, and reliability. Collaborate with Data Engineering to expose governed metrics, dimensions, relationships, and business logic for AI consumption. Document Intelligence & Knowledge Extraction Design and build AI pipelines that transform complex legal documents into structured, machine-understandable data. Help design and evolve legal data models that capture the key concepts, relationships, and business meaning contained within legal documents. Develop techniques to identify legal concepts, obligations, timelines, financial terms, parties, and other domain-specific information that populate and enrich those data models. Design confidence scoring, validation, and human review workflows that improve extraction quality and support continuous model refinement. AI Platform Evolution Optimize AI workflows for quality, latency, scalability, and cost across the platform. Partner closely with Product, Data Engineering, and legal domain experts to translate customer needs into production AI capabilities. Stay current with advances in applied AI, LLMs, and agentic systems, bringing practical innovations into production where they create measurable customer value. What Success Looks Like: 30-60-90 Days First 30 Days | Learn and Contribute Ramp on the platform, semantic layer, and current AI capabilities and evaluation approach. Help create or improve a natural-language query or retrieval flow. By 60 Days | Build and Improve Own an end-to-end AI capability, including its evaluation harness and quality metrics. Deliver the initial design framework for deconstructed legal documents. Measurably improve answer accuracy or reliability on a defined test set. By 90 Days | Own and Lead Deliver production AI features with strong, measured quality and guardrails. Set a technical direction for a piece of the AI roadmap and drive it forward. Establish yourself as a trusted technical partner who can independently design, buils and improve AI capabilities across the Entegrata platform. Required Qualifications 5+ years of software engineering, with hands-on experience building applied AI, NLP, or ML features in production. Practical experience with LLMs: prompting, retrieval-augmented generation, agents, and tool use. Experience with information extraction, document parsing, classification or named entity recognition. Strong evaluation mindset: building test sets and measuring model and system quality. Solid SQL and understanding of structured data and semantic layers. Proficiency in Python and one of Go, .NET, or Node.js. Focus on accuracy, safety, and reliability in customer-facing AI. Preferred Qualifications Experience with natural-language-to-SQL or query generation over governed data. Familiarity with data lakehouse or BI platforms (Databricks, Snowflake, or Microsoft Fabric). Experience with vector / semantic search, embeddings, reranking and hybrid retrieval. Experience building pipelines and processing approaches for long-form or highly structured documents. Legal, finance, or professional services domain exposure. Technology Stack Python, LLM tooling, RAG, vector search, SQL, Azure, Go/.NET/Node.js. Compensation And Benefits Competitive salary, medical, dental, and vision insurance, 401(k) with employer match, unlimited PTO and company holidays, professional development support, and flexible remote work. Equal Opportunity Entegrata is an equal opportunity employer. We celebrate diversity and are committed to building an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other protected characteristic.