Sr AI/ML Engineer - Remote

UnitedHealth Group•Minnetonka, MN

3d•$120,100 - $214,500•Remote

About The Position

At UnitedHealthcare, we’re simplifying the health care experience, creating healthier communities and removing barriers to quality care. The work you do here impacts the lives of millions of people for the better. Come build the health care system of tomorrow, making it more responsive, affordable and optimized. Ready to make a difference? Join us to start Caring. Connecting. Growing together We are seeking a Sr AI/ML Engineer to join our LMIS team in supporting UnitedHealthcare Employer & Individual (E&I) modernization and our underwriting team. In this role, you will design, build, and optimize cutting-edge AI and machine learning solutions that directly improve underwriting workflows, simplify the healthcare benefits planning experience, and modernize core legacy systems. You will work within a collaborative environment leveraging a modern technology stack, including Python, high-performance web APIs, Large Language Models (LLMs), retrieval-augmented generation (RAG) pipelines, Model Context Protocol (MCP), and agent-to-agent communication protocols. Your work will be highly impactful, helping to automate complex underwriting processes, streamline benefit assessments, and deploy production-ready AI models and agentic AI capabilities to enhance operational efficiency. If you are passionate about applying artificial intelligence to transform underwriting processes and accelerate health benefits modernization, join us to start Caring. Connecting. Growing together. 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

5+ years of software engineering, system integration, or machine learning engineering experience
3+ years of professional experience developing and deploying robust web APIs (such as FastAPI, Flask, or RESTful services) in production environments
3+ years of experience programming in Python, including standard machine learning libraries (such as NumPy, Pandas, Scikit-Learn, PyTorch, or TensorFlow)
1+ years of hands-on experience integrating or working with Large Language Models (LLMs) or generative AI concepts
Experience designing or implementing retrieval-augmented generation (RAG) pipelines, including embeddings, vector databases, semantic search, and LLM orchestration
Driver’s License and access to reliable transportation

Nice To Haves

Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field (or equivalent years of experience)
Experience in health insurance, financial services, or supporting underwriting systems and technologies
Experience utilizing generative AI development tools (such as LangChain, Hugging Face, or prompt engineering frameworks) to build application solutions
Experience with vector databases, knowledge retrieval patterns, prompt grounding, tool calling, or enterprise search integration
Familiarity with agent orchestration frameworks, multi-agent systems, MCP servers/clients, and secure agent-to-agent communication design patterns
Familiarity with MLOps frameworks, model performance monitoring, and model registries.
Experience with cloud platform environments (e.g., Azure, AWS, GCP) and containerization tools (such as Docker or Kubernetes)
Familiarity with CI/CD deployment pipelines and git-based source control workflows
Excellent communication and collaboration skills with the ability to articulate technical concepts to both technical and non-technical stakeholders

Responsibilities

Design, develop, and deploy AI-powered solutions and platforms to address complex business challenges with a solid emphasis on the responsible use of AI
Design and implement retrieval-augmented generation (RAG) pipelines that integrate enterprise knowledge sources, vector search, embeddings, and LLM orchestration patterns
Support agentic AI architectures by integrating Model Context Protocol (MCP) capabilities and agent-to-agent communication protocols for secure, interoperable workflow execution
Leverage enterprise-approved AI tools to enhance productivity and innovation by streamlining workflows and automating repetitive engineering tasks
Evaluate emerging trends and approved toolsets in the AI/ML landscape to inform solution design, architectural decisions, and strategic underwriting and modernization initiatives
Design, develop, and optimize robust AI/ML capabilities using Python to address complex underwriting and benefit determination business needs
Build, integrate, and maintain high-performance, secure API endpoints to expose machine learning models and LLM functionalities to core underwriting systems
Integrate Large Language Models (LLMs) and advanced AI model architectures into production environments to enhance user workflows, benefit assessments, and operational efficiency
Collaborate with software engineers, data engineers, and underwriting stakeholders to deliver scalable machine learning pipelines and microservices.
Ensure high availability, security, and performance of deployed models through rigorous testing, monitoring, and debugging