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.
Primary Responsibilities: - 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 injection, sensitive-data leakage, bias, and unsafe actions with documented evaluations, guardrails, fallback paths, and human oversight.
- Set technical direction through working implementations. Lead cross-team architecture decisions and create reusable services, reference implementations, and engineering standards. Mentor senior engineers through code and design reviews while remaining directly accountable for critical technical delivery.
- Deliver at pace with sound judgment. Break ambiguous initiatives into small, demonstrable releases, adapt quickly as evidence and priorities change, and surface blockers early. Balance experimentation speed with production quality and track outcomes against agreed business and engineering measures.
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Required Qualifications: - Bachelor's degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience demonstrated through the engineering delivery requirements below
- 12+ years of software engineering experience
- 8+ years of experience writing, testing, and maintaining production application or platform code
- 3+ years of experience designing, deploying, and operating AI/ML capabilities in production, including ownership of model or service integration, evaluation, release controls, monitoring, and ongoing improvement
- 3+ years deploying and operating services on at least one cloud platform, including CI/CD, logs, metrics, traces, incident diagnosis, and release or rollback procedures. Must have implemented access controls, secrets management, and safeguards for sensitive data
- 2+ years of experience designing and shipping LLM-powered applications or agentic systems, including RAG systems and operational agent workflows that incorporate tool integration, multi-step orchestration, state management, permission controls, failure recovery, and human approval where needed
- 2+ years of experience using LLM-assisted coding tools to design, implement, and iterate on complex software systems
Preferred Qualifications: - Experience delivering healthcare applications or AI solutions involving protected health information or other regulated data, including privacy controls, auditability, and responsible AI evaluations
- Experience partnering directly with CIO-level executives on technology research, solution demonstrations, investment trade-offs, and production adoption
- Experience creating reusable AI platforms, agent services, reference implementations, or developer tools adopted by multiple engineering teams
- Experience with multi-agent orchestration, agent memory, Model Context Protocol (MCP), or equivalent enterprise tool-integration approaches, including testing and monitoring of multi-step failure modes
- Experience with model adaptation or fine-tuning, open-source model serving, inference optimization, or advanced retrieval and ranking techniques, supported by comparative evaluations
- Experience with containers, Kubernetes, infrastructure as code, model registries, and automated evaluation pipelines supporting repeatable software and model releases
*All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy.
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $164,600 - $282,200 annually based on full-time employment. We comply with all minimum wage laws as applicable.
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
