Sr AI/ML Engineer
- Compensation
- Not disclosed
- Location
- Eden Prairie, Minnesota, United States
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Full time
- Experience
- 2+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 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, Pinecone, Milvus, pgvector, or FAISS), and retrieval evaluation
- 2+ years of experience integrating third-party APIs and building backend services that run in production
- 2+ years of experience with cloud platforms (Azure, AWS, or GCP), containers, and CI/CD pipelines
- 2+ years of experience building LLM evaluation and testing workflows rather than relying on manual spot checks
Nice to have
- Bachelor's degree or equivalent experience (e.g., 4+ years of software engineering or machine learning engineering experience in lieu of a degree)
- Experience with model fine-tuning or parameter-efficient fine-tuning (PEFT) techniques
- Experience integrating agents with enterprise observability and security platforms via APIs and MCP-style tool interfaces
- Proven ability to work across multidisciplinary teams (e.g., SRE, InfoSec, platform engineering) and translate operational pain points into technical requirements
Full description
Our Company
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.Position 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.
Primary Responsibilities
- 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.