Sr Principal AI/ML Engineer: Office-Based preferred, will consider Remote

UnitedHealth Group•Eden Prairie, MN

9d•$184,600 - $316,500•Remote

About The Position

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. Join us in making healthcare work better for everyone through people-led, responsible AI while Caring. Connecting. Growing together. As the Senior Principal AI/ML Engineer for GTS, you will drive the architectural vision, technical design, and hands-on delivery of scalable, resilient, and secure software and artificial intelligence/machine learning (AI/ML) solutions that support healthcare applications and workflows. Combining deep software engineering expertise with applied AI/ML engineering, you will build cloud-native services, reusable components, and robust application programming interfaces (APIs) alongside machine learning models, generative AI solutions, retrieval-augmented generation (RAG), and AI agents that enable workflow automation and decision-making. You will advance the adoption of AI-powered development tools and cloud AI services, establish reusable engineering patterns, and develop automated pipelines for model evaluation, deployment, and monitoring. Your solutions will meet security, reliability, scalability, and responsible AI expectations. You will own complex technical initiatives from experimentation through production, resolve architectural challenges across teams, and partner with product, data, security, and business leaders to translate priorities into measurable outcomes. In this senior individual contributor role, you will remain actively involved in coding, prototyping, and technical reviews while mentoring engineers and influencing technical direction across GTS. You will provide leadership through deep expertise and cross-team collaboration rather than direct people management. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges.

Requirements

Bachelor’s degree in computer science, software engineering, a related technical field, or equivalent practical experience
15+ years of relevant engineering experience, including 10+ years of hands-on software engineering and 6+ years delivering production AI/ML solutions
Hands-on depth in cloud-native architecture, automated testing, CI/CD, IaC, observability, secure design, performance tuning, and production incident resolution
Demonstrated ability to integrate and evaluate ML and LLM solutions, including generative AI, RAG, and agents, and to implement deployment, versioning, monitoring, and responsible AI controls
Demonstrated delivery of complex, production-grade distributed services, APIs, data integrations, and reusable platforms, with strong coding skills in Python and at least one additional general-purpose language
Evidence of cross-team technical leadership on ambiguous initiatives: influencing architecture and roadmaps, resolving dependencies and risks, mentoring engineers, and explaining trade-offs clearly to technical and business audiences

Nice To Haves

Experience with healthcare applications, clinical or administrative workflows, sensitive health data, or other regulated environments
Experience with one or more major cloud platforms and managed AI services, such as Azure, Amazon Web Services, or Google Cloud
Experience with relevant tools such as PyTorch, TensorFlow, scikit-learn, agent orchestration frameworks, vector databases, containers, and Kubernetes; equivalent technologies are welcome
Experience scaling shared engineering or AI platforms across teams, conducting rigorous offline and online evaluations, or measuring the impact of AI-assisted development

Responsibilities

Define architecture for complex initiatives spanning multiple teams. Turn business needs into technical designs, phased delivery plans, and measurable quality goals while resolving dependencies, technical risks, and competing design priorities
Design and build distributed, cloud-native services, robust APIs, reusable libraries, and shared platforms. Establish integration contracts and engineering patterns that teams can adopt across healthcare applications and workflows
Create working prototypes, evaluate design alternatives, and lead technical and code reviews. Improve testability, maintainability, and automated unit, integration, contract, and end-to-end testing
Build reliable data integrations and event-driven workflows. Address data quality, schema evolution, failure recovery, and access controls across application, enterprise, and AI data sources
Advance continuous integration and continuous delivery (CI/CD), infrastructure as code (IaC), and automated security and quality checks. Create repeatable deployment patterns with controlled releases, rollback paths, and clear operational readiness criteria
Own production readiness and ongoing technical health. Define service-level objectives, observability, alerts, and runbooks; diagnose incidents, lead root-cause analysis, and implement improvements that prevent recurrence
Optimize performance, scalability, resilience, and cloud cost. Apply secure design, least-privilege access, secrets management, and threat modeling while balancing delivery speed with long-term reliability
Influence technical roadmaps and engineering standards across GTS. Document architectural decisions, guide platform reuse, and mentor engineers through pairing, design discussions, and reviews without direct management responsibilities
Develop and integrate machine learning and large language model (LLM) solutions, including generative AI, RAG, and vector retrieval. Select approaches based on workflow needs, data readiness, accuracy, latency, cost, and operational constraints
Build AI agents with orchestration, tool calling, and guardrails. Define permission boundaries, validate tool inputs and outputs, prevent prompt injection and unintended data exposure, and provide human review for consequential actions
Establish model, prompt, and retrieval evaluations using representative datasets and clear acceptance criteria. Compare AI solutions with simpler baselines; assess factual grounding, task success, bias, safety, and failure modes before release
Implement machine learning and LLM operations (MLOps/LLMOps) pipelines. Version models, prompts, datasets, and retrieval configurations; automate deployment, monitor quality and drift, and support traceable releases and rollbacks
Guide adoption of AI-powered development tools and cloud AI services through hands-on trials and reusable patterns. Measure engineering value, validate generated code, and maintain testing, security, and intellectual property safeguards
Partner with product, data, security, and business leaders on responsible AI delivery. Protect sensitive data, assess privacy and bias risks, define human oversight and fallback behavior, and resolve cross-team risks from experimentation through production