Overview

City/State Virginia Beach, VAWork Shift First (Days)Overview:Sentara is hiring a Senior MLOps & Generative AI Engineer!This position is fully remote!Candidates must reside in one of the following states:Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Washington, West Virginia, Wisconsin, or Wyoming.OverviewWe are seeking a highly skilled and experienced Senior MLOps & Generative AI Engineer to join our growing AI organization and help advance current and future initiatives applying machine learning, deep learning, NLP, and Generative AI technologies to improve healthcare outcomes and operational excellence.This role combines two critical focus areas:MLOps Engineering - building and scaling enterprise-grade ML infrastructure, deployment pipelines, observability, governance, and automation capabilities.Generative AI Engineering - designing, architecting, deploying, and optimizing secure, production-ready GenAI applications and platforms leveraging LLMs, RAG architectures, vector databases, prompt orchestration, and AI evaluation frameworks.As a Senior Engineer, you will partner closely with AI Scientists, Data Engineers, Software Engineers, Architects, and Product teams to operationalize AI/ML and Generative AI solutions at enterprise scale. You will play a key role in shaping the organization's AI platform strategy, driving best practices, and delivering scalable, secure, and reliable AI systems in production healthcare environments.Key ResponsibilitiesMLOps Engineering ResponsibilitiesDesign, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management.Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments.Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation.Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads.Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability.Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes.Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code.Identify gaps and improvement opportunities within the organization's ML platform ecosystem and architect scalable solutions to address them.Support enterprise AI governance, compliance, auditability, and model risk management requirements.Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems.Generative AI Engineering ResponsibilitiesLead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems.Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies.Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability.Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases.Build AI evaluation and benchmarking frameworks to measure hallucination