Senior MLOps & Generative AI Engineer - Remote

Role overview

Sentara Health is hiring a Senior MLOps & Generative AI Engineer (Fully Remote). The role combines two focus areas:

• MLOps Engineering: Build and scale enterprise-grade ML infrastructure, deployment pipelines, observability, governance, and automation.
• Generative AI Engineering: Architect, deploy, and optimize secure, production-ready GenAI applications using LLMs and RAG.

You will partner with AI Scientists, Data Engineers, Software Engineers, Architects, and Product teams to operationalize AI/ML and GenAI solutions at enterprise scale.

Key responsibilities

MLOps Engineering

• Design, build, and maintain scalable ML infrastructure and pipelines for training, deployment, monitoring, governance, and lifecycle management.
• Develop and optimize CI/CD pipelines for ML/AI workloads across development, staging, and production.
• Create reusable ML platform capabilities (feature stores, model registries, experimentation frameworks, artifact management, deployment automation).
• Implement orchestration/workflows for batch and real-time inference.
• Build monitoring to measure performance, detect model drift, monitor data quality, and ensure reliability.
• Develop automation and self-service tools to improve MLOps efficiency and scalability.
• Apply engineering best practices to AI/ML systems (testing, observability, resiliency, security, versioning, Infrastructure as Code).
• Support enterprise AI governance, compliance, auditability, and model risk management.

Generative AI Engineering

• Lead architecture, design, and deployment of enterprise GenAI solutions using LLMs/foundation models and agentic AI systems.
• Design and implement RAG pipelines (vector databases, embeddings, semantic search, reranking, retrieval optimization).
• Build scalable LLM orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel, or equivalents).
• Develop prompt engineering strategies (prompt chaining, context management, agent workflows).
• Evaluate and implement fine-tuning / parameter-efficient tuning / prompt optimization for domain use cases.
• Build GenAI evaluation frameworks to assess quality, accuracy, and reliability.

Requirements

• Senior-level experience delivering ML and/or GenAI systems into production.
• Strong MLOps background: pipelines, orchestration, monitoring, governance, and lifecycle management.
• Strong GenAI background: LLM systems and RAG architecture, orchestration frameworks, and evaluation.

Location / eligibility

• Fully remote, but candidates must reside in an approved list of states (including AL, DE, FL, GA, ID, IN, KS, LA, ME, MD, MN, NE, NV, NH, ND, OH, OK, PA, SC, SD, TN, TX, UT, WA, WV, WI, WY).