Principal AI​/ML Engineer Remote Nationwide or Office-Based in MN​/DC​/NJ

Optum Tech is a global leader in health care innovation. Our teams develop cutting-edge solutions that help people live healthier lives and help make the health system work better for everyone. From advanced data analytics and AI to cybersecurity, we use innovative approaches to solve some of health care's most complex challenges. Your contributions here have the potential to change lives.

Ready to build the next breakthrough? Join us in making healthcare work better for everyone through people-led, responsible AI while Caring. Connecting. Growing together.

As the Principal AI/ML Engineer for GTS, you will serve as a director-level, hands-on technical leader who researches, designs, builds, and ships enterprise AI solutions for healthcare applications and workflows. This is a senior individual contributor role with enterprise-wide influence. You will personally write production code, solve complex engineering problems, and own delivery from early experimentation through secure, reliable production operation.

Working in a fast-moving environment with evolving priorities and ambiguous requirements, you will engage frequently with CIO-level executives and senior business and technology leaders. You will translate strategic priorities into focused research questions, working demonstrations, and production-ready solutions; communicate evidence, limitations, and architectural trade-offs; and recommend when to scale, refine, or stop an approach. Your scope includes generative AI, large language models (LLMs), agentic systems, retrieval-augmented generation (RAG), and applied machine learning.

You will use LLM-assisted coding to accelerate the design and implementation of complex systems while maintaining rigorous standards for correctness, security, maintainability, and responsible AI.

You’ll enjoy the flexibility to work remotely
* from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

• Partner directly with executive leadership. Engage frequently with CIO-level stakeholders to identify high-value opportunities, clarify business constraints, and shape technical priorities. Present working solutions, research findings, delivery options, risks, and recommendations in clear, decision-oriented language.
• Lead rapid applied research and experimentation. Evaluate emerging models, agent architectures, frameworks, and AI development tools through time-boxed experiments. Establish baselines and success criteria, test feasibility with representative data, and make evidence-based build, buy, or reuse decisions.
• Build and ship production AI systems. Personally design, implement, test, and deploy AI-enabled services and applications. Own the transition from prototype to production, including enterprise integration, performance, reliability, scalability, and ongoing operational support.
• Engineer reliable agentic workflows. Build agents that plan, retrieve context, use tools, and execute multi-step tasks within defined boundaries. Implement orchestration, state and memory management, structured outputs, retries, timeouts, failure recovery, and human approval for consequential actions; use multi-agent patterns where justified.
• Use LLM-assisted coding as a core engineering practice. Apply AI coding tools to explore architectures, implement complex features, generate and refine tests, debug, and refactor. Supply appropriate context, work in reviewable increments, and independently validate generated code, dependencies, security, and behavior before release.
• Develop grounded, measurable AI capabilities. Build RAG and semantic-search pipelines with appropriate ingestion, retrieval, ranking, and access controls. Select and adapt models, prompts, and tools based on demonstrated task performance, using conventional ML or simpler software approaches when they better meet the need.
• Establish evaluation and continuous improvement. Create representative test datasets, automated evaluations, and regression suites covering task completion, tool-call correctness, groundedness, safety, latency, and cost. Use production telemetry and structured feedback to improve models, prompts, retrieval, and workflows.
• Embed security, privacy, and responsible AI. Implement least-privilege tool access, secrets management, safe data handling, audit trails, and appropriate execution isolation. Address prompt…