AI/ML Engineer-GenAI & ML Ops

Senior Associate AI/ML — Remote Type of Job : New Contracting Role Req # to submit to : 2026-153569

offices in: San Francisco, CA or Arlington, VA or Westminster, CO or Chicago, IL or Boston, MA or Birmingham, MI or Houston, TX or Atlanta, GA or Miami, FL or New York, NY or Minneapolis, MN or Los Angeles, CA or Seattle, WA or Dallas/Irving, TX or Irvine, CA.

Openings : 3

Top Skills Required

Build and deploy ML/GenAI pipelines Work on RAG, APIs, orchestration workflows Skills: AI/ML, AWS, MLOps Focus: implementation + productionization Expected Start Date : ASAP End Date : 6-months from start date Location : This position does not require candidates to work on-site.

Please aim to find candidates who live close to a PS office in case they are converted over permanently. Must work EST or CST hours AI/ML Engineer - GenAI & MLOps

Your Impact

• Design, build, and deploy ML and Generative AI pipelines from development through production.
• Implement RAG (Retrieval-Augmented Generation) solutions to enhance AI-driven applications with contextual data.
• Develop and integrate APIs and orchestration workflows to support scalable AI systems.
• Drive end-to-end productionization of AI/ML models, ensuring reliability, scalability, and performance.
• Partner with cross-functional teams (engineering, data, product) to operationalize AI use cases in real-world environments.
• Optimize model performance, monitoring, and lifecycle management within cloud-native environments.

Skills & Experience

• Strong hands-on experience in AI/ML engineering, including building and deploying models in production.
• Proven experience with Generative AI frameworks and RAG architectures.
• Expertise in MLOps practices (model deployment, monitoring, CI/CD for ML workflows).
• Experience building and consuming REST APIs and designing workflow orchestration pipelines.
• Strong experience with AWS (SageMaker, Lambda, S3, ECS/EKS, or similar services).
• Proficiency in Python and common ML/AI libraries (e.g., PyTorch, TensorFlow, LangChain, etc.).
• Experience with data pipelines and distributed systems.
• Familiarity with tools like Docker, Kubernetes, and workflow orchestrators (Airflow, Step Functions, etc.).

Strong focus on implementation and production delivery, not just modeling/research.