Sr AI/ML Engineer

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, and data they need to feel their best. Here, you will find a culture guided by diversity and inclusion, talented peers, comprehensive benefits, and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together.

Summary As a Senior AI/ML Engineer, you will play a key role in building and deploying production-grade Large Language Model (LLM) agents and Retrieval-Augmented Generation (RAG) pipelines that automate detection, triage, and remediation of platform and application issues. Working at the intersection of machine learning engineering, system observability, and developer experience, you will design robust orchestration, guardrails, and evaluation frameworks to deliver high-impact AI capabilities. You will partner closely with SRE, InfoSec, and platform teams to build scalable, secure, and responsible AI solutions integrated directly into existing software development workflows. You'll enjoy the flexibility to telecommute* from anywhere within the U.S. as you take on some tough challenges.

Design, develop, and deploy AI-powered solutions to address complex business challenges with an emphasis on responsible use of AI. Build and deploy production LLM agents that automate detection, triage, and remediation of platform and application issues. Design and tune RAG pipelines over runbooks, incident history, logs, code, and internal documentation, including chunking strategy, embedding selection, vector store design, hybrid/semantic search, and reranking. Implement agent orchestration, including tool/function calling, multi-step planning, multi-agent handoffs, memory, and state management. Build supporting infrastructure and software layers for agents, including tool wrappers, guardrails, human-in-the-loop approval paths, and fallback behavior. Own model and prompt engineering, including model selection, prompt and context optimization, output structuring, cost/latency tuning, and fine-tuning or parameter-efficient techniques where warranted. Build evaluation pipelines and define quality metrics for agent and RAG behavior (retrieval precision/recall, groundedness, hallucination/false-positive rate, escalation rate, MTTR impact) and iterate based on results. Integrate agents with observability and security platforms (alerting, logging, metrics, vulnerability, and incident data) through APIs and Model Context Protocol (MCP) tool interfaces to deliver capabilities inside developer workflows. Evaluate emerging AI trends and contribute reusable patterns, components, and documentation across the enterprise AI development lifecycle. Use enterprise-approved AI tools to streamline workflows, automate tasks, and drive continuous improvement. Design, develop, and deploy AI-powered solutions to address complex business challenges with emphasis on responsible use of AI You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

4+ years of software engineering or machine learning engineering experience 4+ years of experience with Python, including working knowledge of agent frameworks (e.g., LangGraph, LangChain, LlamaIndex, Semantic Kernel, or CrewAI) 2+ years of hands-on experience building with LLMs, including prompt engineering, tool/function calling, and agent or multi-step orchestration 2+ years of experience designing and shipping RAG systems in production, including embeddings, vector databases (e.g., Azure AI Search,