Senior MLOps & Generative AI Engineer

City/State

Work Shift

Overview:

This position is fully remote!

Selected candidates would be required to be onsite for final round of team interview

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.

We 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 organizations AI platform strategy driving best practices and delivering scalable secure and reliable AI systems in production healthcare environments.

Key Responsibilities

MLOps Engineering 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 organizations 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 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 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

• 5 years of relevant experience with a degree (Required)

or

• 7 years of relevant experience without a degree (Required)

• Experience in lieu of Bachelors Degree.

• No specific certification or licensure requirements

• 5 to 7 years of relevant experience

We provide market-competitive compensation packages inclusive of base pay incentives and benefits. The base pay rate for Full Time employment is: $91416.00 - $152380.80. Additional compensation may be available for this role such as shift differentials standby/on-call overtime premiums extra shift incentives or bonus opportunities.

Keywords: Talroo-IT MLOps Gen AI LLM AWS Azure GCP AI/ML Python PyTorch Hugging Face Transformers TensorFlow RAG EPIC HIPAA AI Governance

Benefits: Caring For Your Family and Your Career

Medical Dental Vision plans

Adoption Fertility and Surrogacy Reimbursement up to $10000

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 $5250/year and discounted educational opportunities through Guild Education

Student Debt Pay Down $10000

Pet Insurance
Legal Resources Plan
Colleagues have the opportunity to earn an annual discretionary bonus ifestablished system and employee eligibility criteria is met.

Sentara Health is an equal opportunity employer and prides itself on the diversity and inclusiveness of its close to an almost 30000-member workforce. Diversity inclusion and belonging is a guiding principle of the organization to ensure its workforce reflects the communities it serves.

In support of our mission to improve health every day this is a tobacco-free environment.

For positions that are available as remote work Sentara Health employs associates in the following states:

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 and Wyoming.