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 & Perks Sentara Health offers a comprehensive benefits package designed to support employees and their families, including: • Medical, dental, and vision insurance • Adoption, fertility, and surrogacy reimbursement of up to $10,000 • Paid Time Off and sick leave • Paid parental and family caregiver leave • Emergency backup care • Short-term and long-term disability coverage • Critical illness coverage • Life insurance • 401(k)/403(b) retirement plans with employer match • Tuition assistance of up to $5,250 per year • Discounted educational opportunities through Guild Education • Student debt pay-down assistance of up to $10,000 • Pet insurance • Legal resources plan • Potential annual discretionary bonus subject to eligibility and established program criteria • Fully remote work opportunity for eligible employees Basic Qualifications • Minimum 5 years of experience building and deploying production software, machine learning systems, or AI platforms. • At least 1 year of hands-on experience developing production Generative AI or LLM-based applications. • Strong programming skills in Python and knowledge of software engineering best practices. • Experience with deep learning and LLM frameworks such as PyTorch, Hugging Face Transformers, TensorFlow, or equivalent technologies. • Hands-on experience implementing RAG architectures, vector search, embeddings, prompt engineering, and LLM orchestration. • Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or equivalent technologies. • Experience deploying AI/ML systems in cloud environments such as AWS, Azure, or GCP. • Strong understanding of APIs, distributed systems, microservices, and scalable backend architectures. • Experience with Kubernetes, containerization, orchestration, and cloud-native infrastructure. • Experience implementing CI/CD pipelines, infrastructure automation, and MLOps practices. • Experience building monitoring, observability, and alerting solutions for AI/ML systems. • Strong understanding of AI/ML lifecycle management, governance, model versioning, and production operations. • Experience designing secure, scalable, production-ready AI platforms and services. • Strong communication and collaboration skills across technical and business teams. Preferred Qualifications • Experience implementing Generative AI and MLOps solutions within healthcare environments. • Experience working with EPIC or healthcare interoperability platforms. • Knowledge of HIPAA, PHI handling, healthcare compliance, and responsible AI practices. • Experience with AI governance frameworks, LLM evaluation methodologies, and AI safety tooling. • Experience optimizing GPU infrastructure and scalable inference architectures. • Familiarity with multi-agent AI systems and autonomous workflows. • Experience with event-driven architectures, streaming pipelines, and real-time inference systems. • Experience with model fine-tuning techniques such as LoRA, PEFT, RLHF, or domain adaptation. • Experience with enterprise AI platform architecture and internal developer platforms. • Previous experience mentoring engineers and leading technical initiatives. • Experience building AI solutions for highly regulated or mission-critical environments. Education & Experience Candidates should meet one of the following pathways: • 5+ years of relevant experience with a degree, or • 7+ years of relevant experience without a degree. Relevant professional experience may be considered in lieu of a Bachelor's degree. Required Experience: 5–7 years of relevant experience. Certification/Licensure: No specific certification or licensure requirements. Compensation The base pay range for full-time employment is $91,416.00–$152,380.80 annually. Additional compensation may be available depending on role eligibility and applicable programs, including shift differentials, standby/on-call pay, overtime, premiums, extra-shift incentives, or bonus opportunities. Equal Opportunity Statement Sentara Health is an equal opportunity employer committed to diversity, inclusion, and belonging. The organization values a workforce that reflects the communities it serves and is committed to creating an environment where employees are respected, supported, and able to contribute their perspectives and talents. Candidates from diverse backgrounds are encouraged to apply. Sentara Health provides reasonable accommodations for qualified individuals with disabilities in accordance with applicable requirements. Sentara Health is also committed to a tobacco-free environment in support of its mission to improve health every day.