Lead AI/ML Engineer

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 are seeking a Lead GenAI Specialist to provide technical leadership for enterprise Generative AI and Agentic AI initiatives. This role is responsible for driving architecture decisions, defining reusable AI platforms and frameworks, guiding complex solution delivery, and accelerating adoption of GenAI capabilities across the organization. Success will be measured through business impact, platform adoption, scalability of AI solutions, reusable enterprise capabilities, and advancement of organizational AI maturity.

AI Strategy and Technical Leadership Define technical direction for enterprise Generative AI and Agentic AI initiatives Lead architecture decisions for complex AI programs and strategic business use cases Establish AI standards, reference architectures, governance models, and engineering best practices Evaluate emerging foundation models, frameworks, and AI platforms to guide enterprise adoption Mentor GenAI Specialists, engineers, and AI practitioners while leading small delivery teams Enterprise GenAI Solution Architecture Design scalable GenAI solutions aligned with business, security, compliance, and operational requirements Architect multi-model solutions leveraging proprietary and open-source foundation models Define reusable patterns for AI assistants, copilots, intelligent search, automation, and decision-support systems Create reference implementations, accelerators, and reusable frameworks to accelerate enterprise adoption Foundation Models and Model Engineering Lead foundation model evaluation, benchmarking, selection, fine-tuning, and deployment strategies Define standards for model adaptation techniques including SFT, LoRA, QLoRA, PEFT, and domain tuning Optimize model quality, latency, throughput, scalability, and cost efficiency Govern model lifecycle processes including evaluation, validation, release management, and production readiness Knowledge Systems Architect enterprise RAG solutions supporting large-scale knowledge retrieval and GenAI applications Design retrieval frameworks including ingestion, chunking, embeddings, indexing, semantic search, reranking, and grounding Establish enterprise patterns for vector search, hybrid retrieval, knowledge graphs, and AI-ready content platforms Define evaluation frameworks to improve retrieval quality, groundedness, and response accuracy Agentic AI and Intelligent Automation Design and govern enterprise Agentic AI architectures Build autonomous and human-in-the-loop workflows using tool integration, planning, reasoning, and orchestration frameworks Lead development of single-agent and multi-agent systems for complex business processes Establish standards for agent memory, context management, state handling, and agent observability AI Platforms, LLMOps and Integration Define architecture for enterprise AI platforms, gateways, model orchestration, and shared AI services Drive LLMOps practices including deployment automation, monitoring, evaluation, governance, and lifecycle management Establish secure integration patterns across applications, APIs, data platforms, and business workflows Partner with platform and engineering teams to operationalize scalable AI capabilities AI Quality, Governance and Responsible AI Define enterprise standards for AI evaluation, observability, monitoring, and quality measurement Establish controls for Responsible AI, model