Senior AI/ML Engineer (Gen AI Specialist)

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. We're looking for a highly skilled Lead Generative AI & Agentic AI Architect to drive the design, development, and enterprise adoption of Large Language Model (LLM), Generative AI, and Agentic AI solutions. This role is responsible for defining enterprise AI architecture, establishing AI platform capabilities, developing domain-specific AI systems, and operationalizing AI solutions that deliver measurable business value at scale. The ideal candidate combines deep expertise in foundation models, AI platforms, retrieval architectures, agentic frameworks, model optimization, and enterprise AI governance. You will partner with product, engineering, security, architecture, and business leaders to build scalable AI platforms, intelligent applications, AI agents, and reusable enterprise capabilities that accelerate AI adoption across the organization. Primary Responsibilities: Enterprise AI Architecture & Strategy Lead the architecture, design, and implementation of enterprise-scale Generative AI and Agentic AI solutions aligned with business objectives Define enterprise AI architecture standards, reference architectures, governance models, and platform capabilities supporting scalable AI adoption Evaluate, benchmark, select, and operationalize proprietary and open-source foundation models based on reasoning capability, response quality, latency, scalability, security, and cost considerations Design multi-model AI architectures and intelligent routing strategies that dynamically select optimal models based on workload requirements, performance characteristics, and operational efficiency Establish reusable AI frameworks, accelerators, and architecture patterns that enable rapid enterprise adoption of Generative AI capabilities Foundation Models & Model Engineering Design, develop, fine-tune, and operationalize foundation models, custom language models, and domain-specific AI solutions Apply advanced model adaptation techniques including: Supervised Fine-Tuning (SFT) LoRA QLoRA PEFT Instruction Tuning Domain Adaptation Optimize model performance through inference tuning, quantization strategies, context management, and workload optimization Manage model lifecycle processes including: Model versioning Validation and testing Release management Production readiness reviews Governance and compliance controls Generative AI & Retrieval Architecture Architect and implement enterprise Retrieval-Augmented Generation (RAG) solutions supporting knowledge-intensive AI applications Design end-to-end retrieval architectures including: Document ingestion Content processing and chunking Embedding generation Vector indexing Semantic retrieval Grounding strategies Retrieval optimization Build AI-ready knowledge platforms that enable accurate, context-aware, and explainable responses Design semantic search, hybrid search, reranking, and vector retrieval frameworks to maximize retrieval quality and answer relevance Establish knowledge retrieval architectures that support enterprise search, copilots, intelligent assistants, and decision-support systems Agentic AI & Intelligent Automation Design and develop Agentic AI systems capable of: Autonomous reasoning Planning and decision-making Task execution Workflow orchestration Tool utilization Build and operationalize single-agent and multi-agent architectures for enterprise use cases Integrate AI agents with: Enterprise applications APIs Databases Knowledge repositories Business workflows External services Design context engineering strategies including: Agent memory management Session persistence Context orchestration Retrieval coordination Long-running conversation management AI Platforms & Enterprise Integration Build enterprise AI platforms that provide centralized access, orchestration, governance, and management across multiple AI model providers Design AI gateway capabilities supporting: Model routing Provider abstraction Policy enforcement Access management Usage governance Cost controls Leverage cloud-based AI platforms including: Azure OpenAI Azure AI Foundry AWS Bedrock Google Vertex AI Comparable enterprise AI ecosystems Design scalable AI services and APIs that enable secure integration across enterprise systems and applications AI Quality, Evaluation & Observability Implement end-to-end AI observability across agentic systems, retrieval frameworks, AI workflows, and foundation model deployments Develop capabilities including: Prompt tracing Agent tracing Workflow tracing Retrieval tracing Inference monitoring Operational telemetry Define and continuously improve AI quality metrics including: Response accuracy Groundedness Hallucination rates Latency Throughput Token consumption Task completion rates User satisfaction Design automated evaluation frameworks leveraging: Benchmark datasets Human feedback Synthetic evaluations Regression testing Production telemetry Responsible AI, Governance & Cost Optimization Establish Responsible AI standards, governance frameworks, and enterprise AI controls. Implement: Safety guardrails Content filtering Data protection controls Prompt injection protection Security policies Compliance requirements Drive AI cost optimization through: Token utilization management Prompt optimization Context optimization Caching strategies Model routing Inference optimization Ensure AI systems meet enterprise requirements for scalability, security, reliability, compliance, and operational excellence Leadership & Stakeholder Management Collaborate with engineering, architecture, security, product, data, and business teams to define AI strategy, standards, roadmaps, and adoption plans Provide technical leadership and guidance for enterprise AI initiatives and strategic programs Mentor engineers, architects, and AI practitioners while promoting AI engineering best practices Drive enterprise AI innovation through reusable frameworks, architecture standards, platform capabilities, and governance models Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities. Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Required Qualifications: Bachelor's degree in computer science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field 8+ years of experience in Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or AI Platform Engineering Proven experience delivering enterprise-scale Generative AI, LLM, and Agentic AI solutions in production environments Experience evaluating, selecting, fine-tuning, deploying, and managing open-source and commercial foundation models Hands-on experience designing and implementing RAG architectures, semantic retrieval systems, and vector-based knowledge platforms Experience building AI platforms, AI gateways, and enterprise AI services operating across multiple foundation model providers Experience implementing AI observability, evaluation frameworks, performance monitoring, and operational governance Experience with cloud-native AI platforms and managed AI services Solid programming experience in Python and modern AI development frameworks Solid understanding of: Transformer architectures Embeddings Tokenization Attention mechanisms Context windows Model performance characteristics Inference optimization Solid understanding of Responsible AI, model governance, safety controls, and enterprise compliance requirements Proven solid expertise in Agentic AI architectures, orchestration frameworks, tool integration, and intelligent workflow automation Proven excellent communication, technical leadership, stakeholder management, and problem-solving skills. Preferred Qualifications: Master's degree Experience architecting enterprise AI platforms supporting Generative AI, Agentic AI, copilots, intelligent assistants, and AI-powered business applications Hands-on experience with LLM orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, Llama Index, AutoGen, CrewAI, or equivalent technologies Experience designing advanced RAG architectures incorporating vector databases, semantic search, hybrid search, reranking, graph-based retrieval, and enterprise knowledge systems Experience building multi-agent systems, agent memory architectures, planning frameworks, and autonomous workflow solutions Experience with vector databases and knowledge platforms such as Pinecone, Weaviate, Chroma, FAISS, pgvector, Azure AI Search, Neo4j, or equivalent technologies Experience implementing AI observability, tracing, evaluation, and monitoring frameworks for production of AI systems Experience building reusable AI accelerators, enterprise AI frameworks, reference architectures, and shared platform services Healthcare, financial services, insurance, or other regulated industry experience Experience mentoring AI engineers, architects, and technical leaders within large enterprise environments Proven deep expertise with Azure OpenAI, Azure AI Foundry, AWS Bedrock, Vertex AI, Anthropic, OpenAI, and open-source model ecosystems Proven solid expertise in AI platform engineering, LLMOps, model lifecycle management, evaluation platforms, and enterprise AI governance Proven expertise in AI security, AI risk management, compliance controls, and responsible AI deployment practices Proven contributions to enterprise AI innovation programs, technical publications, patents, open-source projects, or industry thought leadership initiatives Technical Stack Foundation Models and LLM Platforms (OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, Phi, and similar models) Generative AI Frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI) Model Development and Fine-Tuning Frameworks Retrieval-Augmented Generation (RAG) and Vector Database Technologies AI Evaluation, Observability, and Monitoring Platforms Python-Based AI Application Development and API Integration Cloud AI Platforms (Azure AI Foundry, Azure OpenAI, AWS Bedrock, Vertex AI) Kubernetes, Docker, and Cloud-Native Deployments LLMOps, DevOps, and AI Lifecycle Management AI Gateway, API Management, and Multi-Model Platforms Security, Governance, and Responsible AI Controls At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission. #NIC