AI/ML Engineer WFH

About the Company

Optum Tech is a global healthcare technology organization focused on developing solutions that help people live healthier lives and improve how the healthcare system works. Its teams use data analytics, artificial intelligence, cybersecurity, and other innovative technologies to address complex healthcare challenges. Optum is committed to advancing responsible, people-led AI while caring, connecting, and growing together.

About the Role

Optum Tech is seeking a Principal AI/ML Engineer for its GTS team. This is a director-level, hands-on technical leadership position with enterprise-wide influence. The role involves researching, designing, building, and deploying enterprise AI solutions for healthcare applications and workflows.

As a senior individual contributor, you will write production code, solve complex engineering challenges, and oversee delivery from early experimentation through secure and reliable production operations. You will work closely with CIO-level executives and senior business and technology leaders to turn strategic priorities into practical AI solutions.

The position covers generative AI, large language models (LLMs), agentic systems, retrieval-augmented generation (RAG), and applied machine learning. You will also use LLM-assisted coding tools while maintaining high standards for security, correctness, maintainability, and responsible AI.

Key Responsibilities

• Partner directly with CIO-level executives and senior leaders to identify valuable AI opportunities, clarify business constraints, and establish technical priorities.
• Present working solutions, research findings, delivery options, risks, and recommendations in clear, decision-oriented language.
• Lead rapid applied research and experimentation to evaluate emerging models, agent architectures, frameworks, and AI development tools.
• Establish baselines and success criteria, test feasibility using representative data, and make evidence-based build, buy, or reuse decisions.
• Personally design, develop, test, and deploy AI-enabled services and applications.
• Manage the transition from prototypes to production, including enterprise integration, performance, reliability, scalability, and ongoing operational support.
• Build reliable agentic workflows 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.
• Apply multi-agent patterns when they are appropriate for the use case.
• Use LLM-assisted coding tools to explore architectures, implement complex features, generate and refine tests, debug, and refactor code.
• Provide appropriate context to AI coding tools, work in reviewable increments, and independently validate generated code, dependencies, security, and behavior before release.
• Develop RAG and semantic-search pipelines with appropriate data ingestion, retrieval, ranking, and access controls.
• Select and adapt models, prompts, and tools based on demonstrated task performance, using conventional machine learning or simpler software approaches when they better meet requirements.
• Build 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.
• Implement security and privacy controls, including 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 through documented evaluations, guardrails, fallback paths, and human oversight.
• Lead cross-team architecture decisions and develop reusable services, reference implementations, and engineering standards.
• Mentor senior engineers through code and design reviews while remaining directly accountable for critical technical delivery.
• Break ambiguous initiatives into smaller, demonstrable releases and adapt as evidence and priorities change.
• Identify blockers early, balance experimentation speed with production quality, and track outcomes against agreed business and engineering measures.

Work Arrangement

• Remote work is available from anywhere within the United States.
• Employees hired in the Mi…