Join Optum, a global healthcare organization dedicated to improving lives through technology. We are seeking a Lead Generative AI & Agentic AI Architect to spearhead the design, development, and enterprise-wide implementation of cutting-edge AI solutions. This pivotal role will shape our enterprise AI architecture, establish robust AI platform capabilities, and drive the creation of domain-specific AI systems that deliver significant business value at scale. The ideal candidate possesses deep expertise in foundation models, AI platforms, retrieval architectures, agentic frameworks, and model optimization, working collaboratively with cross-functional leaders to build scalable AI platforms and intelligent applications.

Our mission at UnitedHealth Group is to empower healthier lives and enhance the healthcare system for all. We are committed to fostering an inclusive environment and addressing health disparities, making equitable care a top enterprise priority. Come contribute to advancing health optimization on a global scale.

Lead the architecture, design, and implementation of enterprise-scale Generative AI and Agentic AI solutions, defining architecture standards and governance models. Evaluate, benchmark, and operationalize foundation models, designing multi-model AI architectures and intelligent routing strategies. Develop reusable AI frameworks and accelerators for rapid enterprise adoption. Design, develop, fine-tune, and operationalize foundation models and custom language models, applying advanced adaptation techniques and optimizing model performance through inference tuning and quantization. Manage the full model lifecycle, including versioning, validation, release management, and production readiness reviews. Architect and implement enterprise Retrieval-Augmented Generation (RAG) solutions, designing end-to-end retrieval architectures and building AI-ready knowledge platforms. Develop AI agents capable of autonomous reasoning, planning, and task execution, integrating them with enterprise applications and services. Build enterprise AI platforms providing centralized access, orchestration, and governance across multiple AI model providers, leveraging cloud-based AI platforms and designing scalable AI services and APIs.

Implement end-to-end AI observability across agentic systems, retrieval frameworks, and foundation model deployments, defining and improving AI quality metrics. Establish Responsible AI standards, governance frameworks, and enterprise AI controls, including safety guardrails and prompt injection protection. Drive AI cost optimization through effective token utilization, prompt optimization, and model routing. Collaborate with engineering, security, product, and business teams to define AI strategy and roadmaps, providing technical leadership and mentoring AI practitioners. Uphold ethical AI principles throughout the model development lifecycle.

A Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field is required, alongside a minimum of 8 years of experience in AI, ML, Data Science, Software Engineering, or AI Platform Engineering. Proven success in delivering enterprise-scale Generative AI, LLM, and Agentic AI solutions in production is essential. Candidates must demonstrate hands-on experience in evaluating, selecting, fine-tuning, deploying, and managing foundation models, as well as designing and implementing RAG architectures, semantic retrieval systems, and vector-based knowledge platforms. Experience building AI platforms, AI gateways, and enterprise AI services that span multiple foundation model providers is crucial.

Proficiency in implementing AI observability, evaluation frameworks, performance monitoring, and operational governance is expected. Solid programming skills in Python and modern AI development frameworks are necessary, along with a strong understanding of transformer architectures, embeddings, tokenization, context windows, model performance characteristics, and inference optimization. A thorough grasp of Responsible AI, model governance, safety controls, and enterprise compliance requirements is mandatory. Proven expertise in Agentic AI architectures, orchestration frameworks, tool integration, and intelligent workflow automation, coupled with excellent communication, technical leadership, stakeholder management, and problem-solving skills, will ensure success in this role.