Senior MLOps & Generative AI Engineer - Remote

Sentara Health • Virginia Beach, VA

7d • $91,416 - $152,381 • Remote

About The Position

Sentara is hiring a Senior MLOps & Generative AI Engineer. This position is fully remote and requires candidates to reside in specific states. The role involves advancing initiatives using machine learning, deep learning, NLP, and Generative AI technologies to improve healthcare outcomes and operational excellence. The position combines MLOps Engineering (building and scaling ML infrastructure, deployment pipelines, observability, governance, and automation) and Generative AI Engineering (designing, architecting, deploying, and optimizing GenAI applications and platforms leveraging LLMs, RAG architectures, vector databases, prompt orchestration, and AI evaluation frameworks). The Senior Engineer will collaborate with various teams to operationalize AI/ML and Generative AI solutions at enterprise scale, shaping the organization’s AI platform strategy and delivering scalable, secure, and reliable AI systems in production healthcare environments.

Requirements

• 5+ years of experience building and deploying production software, ML systems, or AI platforms.
• 1+ years of hands-on experience building production Generative AI or LLM-based applications.
• Strong programming skills in Python and experience with software engineering best practices.
• Experience with major deep learning and LLM frameworks such as PyTorch, Hugging Face Transformers, TensorFlow, or equivalent.
• Hands-on experience implementing RAG architectures, vector search, embeddings, prompt engineering, and LLM orchestration frameworks.
• Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, Milvus, or equivalent technologies.
• Experience deploying AI/ML systems in cloud environments including 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 best practices.
• Experience building monitoring, observability, and alerting solutions for ML and AI 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 with the ability to work across technical and business teams.
• 5+ years of relevant experience with a degree (Required) or 7+ years of relevant experience without a degree (Required). Experience in lieu of Bachelor’s Degree.

Nice To Haves

• Previous experience implementing Generative AI and MLOps solutions within healthcare environments.
• Experience working with EPIC or healthcare interoperability platforms.
• Understanding of HIPAA, PHI handling, healthcare compliance, and responsible AI practices.
• Experience with AI governance frameworks, LLM evaluation methodologies, and AI safety tooling.
• Experience with GPU infrastructure optimization 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.
• Exposure to model fine-tuning techniques including LoRA, PEFT, RLHF, or domain adaptation strategies.
• Experience with enterprise AI platform architecture and internal developer platforms.
• Prior experience mentoring engineers and leading technical initiatives.

Responsibilities

• 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.
• 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.
• 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 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 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 and continuous improvement.
• 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 deployment of GenAI solutions involving PHI and sensitive healthcare data.
• Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.

Benefits

• Medical, Dental, Vision plans
• Adoption, Fertility and Surrogacy Reimbursement up to $10,000
• Paid Time Off and Sick Leave
• Paid Parental & Family Caregiver Leave
• Emergency Backup Care
• Long-Term, Short-Term Disability, and Critical Illness plans
• Life Insurance
• 401k/403B with Employer Match
• Tuition Assistance – $5,250/year and discounted educational opportunities through Guild Education
• Student Debt Pay Down – $10,000
• Reimbursement for certifications and free access to complete CEUs and professional development
• Pet Insurance
• Legal Resources Plan
• Colleagues have the opportunity to earn an annual discretionary bonus if established system and employee eligibility criteria is met.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior