Principal AI/ML Engineer - Enterprise Architect

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

As the Principal AI/ML Engineer for GTS , you will drive the architectural design, hands-on development, and production delivery of scalable, resilient, and secure software solutions that integrate AI/ML capabilities into healthcare applications and workflows. Combining deep software engineering expertise with applied AI/ML engineering, you will build cloud-native services, reusable components, and robust APIs that embed intelligent capabilities into enterprise applications. You will develop and integrate machine learning models, generative AI solutions, retrieval-augmented generation (RAG), and AI agents that support workflow automation and decision-making. Using AI-powered development tools and cloud AI services, you will accelerate engineering delivery and establish automated pipelines for model evaluation, deployment, and monitoring, ensuring solutions are secure, reliable, scalable, and aligned with responsible AI practices. In this principal technical role, you will guide engineering decisions across teams, mentor engineers, and advance software modernization and engineering excellence turning promising AI/ML use cases into reliable, maintainable production solutions that deliver measurable

Primary Responsibilities:

• Translate business needs into measurable ML tasks. Validate data, establish simple baselines, and integrate suitable models. Demonstrate whether AI improves outcomes over simpler approaches
• Integrate generative AI and large language models (LLMs) where they add value. Use retrieval-augmented generation (RAG), which supplies relevant source content to a model, when appropriate. Evaluate accuracy, unsupported claims, latency, and cost
• Build model-serving interfaces and machine learning operations (MLOps) workflows for versioning, deployment, monitoring, and rollback
• Track changes in data and model performance, then improve models, prompts, or product design
• Apply responsible AI practices through documented evaluations, bias and robustness checks, privacy safeguards, and human review where needed. Address prompt injection and sensitive-data leakage, define safe fallback behavior, and explain model limitations
• Design and build distributed services, application programming interfaces (APIs), and reliable data integrations. Make clear trade-offs across scalability, performance, cost, and maintainability
• Write and review production code, automate unit and integration tests, and build continuous integration and delivery (CI/CD) pipelines for safe, repeatable cloud releases
• Build security and privacy into software design through access controls, secrets management, and safe data handling. Identify technical risks before release and address them with product and security partners
• Own production readiness and service health through logs, metrics, and traces. Troubleshoot incidents, remove recurring failure points, and improve reliability and response times
• Lead cross-team architecture decisions and deliver reusable components. Document trade-offs, mentor engineers, and break complex requirements into manageable releases that meet agreed product and quality goals
• Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements

Required Qualifications:

• Bachelor's degree in computer science, engineering, mathematics, or a related field, or equivalent practical experience
• 15+ years of relevant engineering experience, including 8+ years of hands-on software engineering and 3+ years delivering AI/ML capabilities in production
• Experience taking ML or generative AI capabilities from evaluation to production, including model integration, deployment, monitoring, and measurable quality assessment
• Solid coding skills in at least one production backend language, with working proficiency in Python for AI/ML development
• Demonstrated delivery of distributed systems, APIs, data integrations, automated tests, and cloud deployments, including practical security and operational ownership
• Ability to explain technical decisions clearly, influence architecture across teams, and mentor engineers while continuing to contribute production code

Preferred Qualifications:

• Experience building healthcare applications or working with other regulated or sensitive data
• Experience delivering shared services or platforms used by multiple engineering teams
• Experience with containers, infrastructure as code, or model registries to support reliable software and model releases
• Experience with vector search or RAG evaluation, and familiarity with an established ML framework.

At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.