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

UnitedHealth Group•Basking Ridge, NJ

1d•$120,100 - $214,500•Hybrid

About The Position

The Provider Digital Technology team delivers a unified, intuitive digital entry point for UnitedHealthcare providers and partners to access tools, services, and information. We build secure, scalable capabilities that simplify how providers discover, navigate, and engage across the Portal, API, EDI, Chat, and Voice channels. Our AI/ML engineering team develops production-grade intelligent solutions, including generative AI assistants, retrieval-augmented generation (RAG), agentic workflows, conversational AI, predictive models, and intelligent automation. We partner across UnitedHealthcare and UnitedHealth Group to integrate enterprise applications and data sources while applying responsible AI, privacy, security, safety, observability, and human-centered design principles. This work reduces friction, improves provider workflows, and enables faster, more effective problem-solving across the health care ecosystem. 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. For all hires in New Jersey, you will be required to work in the office a minimum of three days per week.

Requirements

BS in Computer Science, Engineering, or related technical field (or equivalent experience)
6+ years of software engineering experience
4+ years of hands-on experience designing and building AI/ML solutions, including LLM applications, Generative AI, NLP, predictive models, or traditional machine learning
4+ years of software engineering experience with web services, RESTful APIs, microservices, and system integration
4+ years of experience deploying and operating applications in cloud environments; AWS experience preferred
3+ Solid programming experience in Python or Java, including clean code, automated testing, source control, and modern software engineering practices
2+ Hands-on experience with chatbots, conversational AI, NLP, RAG, embeddings, vector databases, and semantic search patterns
1+ Hands-on experience with Agentic AI, tool-calling workflows, MCP or similar orchestration frameworks, and Strands Agents

Nice To Haves

Proven solid knowledge of AI/ML production operations, including security, access controls, responsible AI guardrails, evaluation, logging, monitoring, performance tuning, disaster recovery, and incident support
Experience delivering production AI/ML solutions in the health care industry or another highly regulated environment
Full-stack engineering experience and familiarity with modern web frameworks, cloud-native architectures, and infrastructure-as-code
Experience with LLM evaluation, prompt testing, RAG quality measurement, model monitoring, and AI observability
Experience with MLOps or LLMOps, CI/CD, containers, Kubernetes, feature or model lifecycle management, and production telemetry
Proven knowledge of data engineering, vector stores, enterprise search, and secure integration of structured and unstructured data sources

Responsibilities

Lead the responsible adoption of AI/ML across the team, applying Generative AI, Agentic AI, NLP, predictive modeling, and traditional machine learning to high-value provider use cases
Design, build, evaluate, deploy, and continuously improve production-grade AI/ML services, APIs, and applications that meet customer, business, reliability, and scalability needs
Engineer responsible AI solutions with appropriate guardrails, privacy and security controls, human oversight, auditability, and compliance with enterprise and regulatory standards
Develop LLM-powered capabilities using prompt engineering, retrieval-augmented generation (RAG), embeddings, vector search, tool use, structured outputs, and model evaluation techniques
Provide technical leadership for Agentic AI, chatbots, voice assistants, conversational AI, and workflow automation, including orchestration through MCP or similar frameworks
Partner with product managers, architects, data scientists, platform teams, and engineering leaders to translate complex health care needs into scalable AI solutions
Align solution architecture with enterprise AI strategy, reusable platform capabilities, engineering standards, cloud modernization, and measurable business outcomes
Establish high-quality engineering practices for experimentation, automated testing, model and prompt evaluation, CI/CD, observability, performance, resiliency, and production support
Mentor engineers on AI/ML architecture, Python and Java development, responsible AI, LLM application patterns, evaluation frameworks, and operational excellence
Integrate AI capabilities with enterprise data, APIs, event-driven services, cloud platforms, and digital channels while maintaining secure and reliable data flows
Exercise independent judgment to prioritize technical work, manage delivery risks, communicate tradeoffs, and drive AI initiatives from proof of concept through production scale