Sentara Healthcare is hiring a Senior MLOps & Generative AI Engineer to enhance their AI organization. They are focused on applying advanced AI technologies to improve healthcare outcomes and operational efficiency within a production environment.

About the Position

Introduction

Sentara is seeking a highly skilled and experienced Senior MLOps & Generative AI Engineer to join its growing AI organization. This fully remote position focuses on advancing initiatives that apply machine learning, deep learning, NLP, and Generative AI technologies to enhance healthcare outcomes and operational excellence.

This role combines expertise in MLOps Engineering—building and scaling enterprise-grade ML infrastructure—and Generative AI Engineering—designing, architecting, deploying, and optimizing secure, production-ready GenAI applications. The Senior Engineer will collaborate with AI Scientists, Data Engineers, Software Engineers, Architects, and Product teams to operationalize AI/ML and Generative AI solutions at an enterprise scale within a production healthcare environment.

Responsibilities

MLOps Engineering Responsibilities

Design, build, and maintain scalable ML infrastructure and pipelines for model training, deployment, monitoring, governance, and lifecycle management.
Develop and optimize CI/CD pipelines for machine learning and AI workloads across various 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.
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 MLOps efficiency, scalability, and reliability.
Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation to 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 ML platform ecosystem and architect scalable solutions.
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 Responsibilities

Lead 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 equivalents.
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 rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics.
Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments.
Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications.
Optimize GenAI 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 GenAI technologies, open-source frameworks, and foundation models to drive innovation.
Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives.
Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployments.

Requirements

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.
Highly skilled and experienced in MLOps and Generative AI.
Experience with machine learning, deep learning, NLP, and Generative AI technologies.
Proficiency in building and scaling enterprise-grade ML infrastructure and deployment pipelines.
Experience with LLMs, RAG architectures, vector databases, prompt orchestration, and AI evaluation frameworks.
Strong collaboration skills to work with AI Scientists, Data Engineers, Software Engineers, Architects, and Product teams.