Lead AI/ML Engineer-Remote Nationwide or Office Based in MN/DC

UnitedHealth Group•Boston, MA

2d•$145,500 - $249,500•Remote

About The Position

Optum Insight is improving the flow of health data and information to create a more connected system. We remove friction and drive alignment between care providers and payers, and ultimately consumers. Our deep expertise in the industry and innovative technology empower us to help organizations reduce costs while improving risk management, quality and revenue growth. Ready to help us deliver results that improve lives? Join us in making healthcare work better for everyone through people-led, responsible AI while Caring. Connecting. Growing together. Our Value Connect team is building an AI-first platform for value-based care that leverages Large Language Models (LLMs), Generative AI, AI agents, intelligent automation, and advanced analytics to improve provider performance, care outcomes, cost management, and operational efficiency. The team is developing capabilities such as conversational AI assistants, agentic workflows, Registry automation, ambient listening, knowledge discovery, and AI-powered decision support. The Lead AI Engineer will provide hands-on technical leadership to build scalable, production-ready AI solutions that transform complex healthcare data into actionable workflows for care, quality, finance, and provider enablement teams. 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.

Requirements

Bachelor's degree with 6+ years, Master's degree with 4+ years, or PhD with 3+ years of relevant software engineering, AI engineering, or related experience
2+ years of hands-on experience building Generative AI applications utilizing LLMs, RAG frameworks, AI agents, conversational AI, or workflow automation solutions
3+ years of professional experience developing production-grade applications using Python and modern software engineering practices
Experience building and deploying AI systems utilizing vector databases, orchestration frameworks (e.g., LangChain, LangGraph), or similar technologies
Experience deploying and operating cloud-native applications on Azure or AWS utilizing containerization technologies
Solid experience with software engineering practices including GitHub, code reviews, automated testing, and CI/CD pipelines
Experience integrating AI solutions with enterprise data platforms and APIs

Nice To Haves

Experience with Databricks, Spark, and large-scale data processing
Experience with traditional machine learning, predictive analytics, or statistical modeling
Experience fine-tuning open-source models or optimizing LLM inference performance
Experience evaluating and benchmarking AI systems for quality, safety, and business impact
Experience with healthcare technology, clinical data processing, or value-based care platforms
Ability to translate complex business requirements into scalable technical solutions and communicate effectively with technical and non-technical stakeholders

Responsibilities

Lead the design, architecture, and implementation of Generative AI, RAG-based, agentic, and intelligent automation solutions
Design and build LLM-powered applications, conversational assistants, workflow automation, and knowledge retrieval systems
Develop and optimize prompts, retrieval strategies, AI agents, evaluation frameworks, and production AI workflows
Develop and maintain production-grade APIs and services to expose AI capabilities at scale.
Implement engineering best practices for AI application lifecycle management, monitoring, observability, governance, security, and reliability
Deploy and operate AI workloads on cloud platforms such as Azure or AWS using containerization, Databricks, PySpark, and modern data platforms
Establish engineering best practices for software quality, testing, responsible AI, design reviews, and code reviews
Partner with product, clinical, operational, and business stakeholders while mentoring engineers and evaluating emerging AI technologies
Leverage enterprise-approved AI tools and technologies to improve developer productivity and accelerate innovation