Sr AI ML Engineer - Remote or Hybrid Eden Prairie, MN or Schaumburg, IL

UnitedHealth Group•Eden Prairie, MN

2d•$120,100 - $214,500•Hybrid

About The Position

Join our innovative technology team at Optum Health Financial Technology dedicated to designing, developing, and deploying high-impact, AI-powered solutions across our Care Management and Clinical platforms. In this senior-level role, you will focus on building agentic AI workflows, LLM-based automations, clinical summarizations, and case preparation tools to optimize clinical workflows. You will design and deploy advanced reasoning systems that leverage enterprise AI platforms, prompt engineering patterns, and retrieval-augmented generation (RAG) models. This role offers an exciting opportunity to build scalable, reusable components that integrate human-in-the-loop decision-making directly into platforms that support patient care, financial technology integration, and overall health outcomes. 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
5+ years of experience in AI/ML engineering, software engineering, data engineering, or automation engineering, with hands-on experience delivering production-grade solutions
3+ years of experience using Python for automation, backend services, API integrations, and code deployment
2+ years of experience building solutions using Large Language Models (LLMs), generative AI, prompt engineering, or retrieval-augmented generation (RAG)
1+ years of experience developing agentic AI systems, including multi-step reasoning workflows, task orchestration, or tool-calling agents
Hands-on experience with modern AI orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or cloud-native AI services)
Experience deploying scalable AI solutions within cloud environments (e.g., Azure, AWS, GCP) using microservices, containers, or serverless architectures

Nice To Haves

Master’s degree in Computer Science, Data Science, Engineering, Artificial Intelligence, or a related field
Experience developing AI solutions specifically for healthcare domains, such as care management, clinical documentation, utilization management, or clinical applications
Experience configuring and integrating vector databases or search platforms (e.g., Azure AI Search, Pinecone, Weaviate, FAISS, Chroma, OpenSearch, or Elasticsearch)
Experience with traditional ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn) combined with LLM-based workflows
Experience with LLM evaluation frameworks, prompt versioning, synthetic data generation, and LLMOps monitoring tools
Solid understanding of security design in regulated environments, including role-based access control, encryption, and safe handling of PII/PHI

Responsibilities

Design, develop, and deploy robust AI-powered solutions and agentic AI workflows to address complex clinical and business challenges, with an emphasis on the responsible and ethical use of AI
Build and implement agentic AI systems that reason over clinical workflows, orchestrate multi-step tasks, interact with APIs and system tools, and integrate human-in-the-loop decision-making frameworks
Develop scalable, reusable AI components using LLM orchestration frameworks, advanced prompt engineering patterns, retrieval-augmented generation (RAG), and vector databases
Use Python as a core engineering language to write production-grade automation scripts, pipelines, backend services, and reusable integration components
Leverage enterprise-approved AI tools and developer platforms to streamline workflows, automate repetitive tasks, and drive continuous platform improvement
Evaluate emerging AI trends, agent patterns, and automation frameworks to inform secure solution design and drive continuous, strategic platform innovation
Collaborate closely with product, clinical, data, architecture, security, and engineering teams to translate complex requirements into secure, compliant, and production-ready capabilities
Support the operationalization, monitoring, observability, and MLOps/LLMOps of AI systems, ensuring compliance with healthcare data privacy, security, and regulatory guidelines