AI Engineer / MLOps, Sr.- Remote

About the Company Sentara Health is a healthcare organization focused on improving health every day through innovative care, technology, and services. With a workforce of nearly 30,000 colleagues, Sentara is committed to building an inclusive workplace that reflects the communities it serves. Sentara is expanding its AI capabilities to support healthcare outcomes and operational excellence through machine learning, deep learning, natural language processing, and Generative AI technologies. About the Role Sentara Health is seeking a highly skilled Senior MLOps & Generative AI Engineer to join its growing AI organization. This fully remote role combines two critical areas of enterprise AI engineering: MLOps Engineering and Generative AI Engineering. The successful candidate will design, build, deploy, and optimize secure, scalable, and production-ready AI/ML platforms and applications. The Senior Engineer will work closely with AI Scientists, Data Engineers, Software Engineers, Architects, Cybersecurity, Infrastructure, and Product teams to operationalize AI and Generative AI solutions at enterprise scale. This position will play a key role in shaping AI platform strategy, establishing engineering best practices, and delivering reliable AI systems for production healthcare environments. Remote Work Eligibility: Candidates must reside in an eligible state, including Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington, West Virginia, Wisconsin, or Wyoming. Selected candidates will be required to attend the final round of team interviews onsite. Key Responsibilities MLOps Engineering Design, 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.Develop monitoring systems to measure model performance, detect model drift, monitor data quality, and maintain production reliability.Create automation and self-service capabilities that improve MLOps efficiency, scalability, and reliability.Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment.Apply software engineering best practices including testing, observability, resiliency, security, versioning, and infrastructure-as-code.Identify gaps within the ML platform ecosystem and architect scalable solutions to address them.Support enterprise AI governance, compliance, auditability, and model risk management requirements.Ensure AI/ML platforms maintain scalability, reliability, security, and operational excellence.Generative AI Engineering Lead the architecture, design, and deployment of enterprise Generative AI solutions using 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.Build scalable LLM orchestration frameworks using LangChain, LlamaIndex, Semantic Kernel, or equivalent technologies.Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows.Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific applications.Build AI evaluation and benchmarking frameworks measuring hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance.Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices.Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.Optimize Generative AI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments.Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows.Research and evaluate emerging Generative AI technologies, open-source frameworks, and foundation models.Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level technical documentation.Collaborate with cybersecurity, compliance, and infrastructure teams to support secure deployment of GenAI solutions involving PHI and sensitive healthcare data.Contribute to AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.Benefits & Per