Lead AI​/ML Platform Engineer - Remote

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. Our Enterprise AI organization builds the platforms that govern and operate AI systems across the business, spanning intake and registration, automated governance gating, request routing, and runtime observability and monitoring. Today, these capabilities operate as distinct components across the AI lifecycle.

We need to connect them end-to-end - linking pre-production governance to post-production monitoring, alerting, feedback, and containment - to meet enterprise AI risk management commitments and committed audit-related deliverables. As a Lead AI/ML Engineer, you will help design and build this end-to-end AI lifecycle governance platform. You will establish technical integrations, close the loop between pre-production gating and runtime observability, and implement core integration components hands-on.

In this role, you will collaborate across intake, governance, routing, and monitoring teams to define data contracts, set technical standards, and build the foundation that scales responsible AI across the enterprise. 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.

• Design, develop, and deploy AI-powered solutions to address complex business challenges with an emphasis on responsible use of AI
• Architect and implement end-to-end AI lifecycle governance capabilities linking intake and registration, automated governance gating, request routing, and runtime observability/monitoring
• Design technical integrations connecting pre-production governance with post-production monitoring, alerting, feedback, and containment
• Define data flows, interfaces, and integration contracts between system components to support lifecycle traceability and compliance
• Build and deploy core integration components through hands-on engineering, automated testing, and low-level design
• Partner with and guide cross-functional engineering teams across intake, governance, routing, and observability to align on technical designs and execution sequencing
• Use enterprise-approved AI tools to streamline workflows, automate tasks, and drive continuous improvement
• Evaluate emerging trends, platforms, and frameworks to inform solution design, continuous improvement, and strategic innovation

• Bachelor's degree or 4+ years of software engineering or AI/ML engineering experience in lieu of a degree
• 5+ years of hands-on experience in AI/ML engineering or software engineering, including building and deploying production-level AI systems
• 3+ years of experience designing multi-component distributed systems or platform architectures
• 3+ years of professional experience developing production-grade services and integrations using Python
• 2+ years of experience designing or implementing systems involving governance, runtime monitoring, observability, or lifecycle management of software/AI systems
• 2+ years of experience leading technical design or guiding engineering teams across complex technical initiatives

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related technical discipline
• Experience with AI observability, model evaluation (evals), or testing frameworks and platforms
• Experience with automated governance gating, policy-as-code, or compliance automation for AI systems
• Experience developing within cloud-native architectures (e.g., Azure, AWS, GCP) and containerized environments (e.g., Docker, Kubernetes)
• Familiarity with human-in-the-loop (HITL) review, feedback mechanisms, and containment workflows for AI systems
• Familiarity with enterprise AI audit or risk management frameworks
• All employees working remotely will be required to adhere to United Health Group's Telecommuter Policy

Pay is based on several factors…