Position SummaryAs 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.
- 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.
Required Qualifications- 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
Preferred Qualifications- 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
*All Telecommuters will be required to adhere to UnitedHealth Group's Telecommuter Policy.
Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $120,100 to $214,500 annually based on full-time employment. We comply with all minimum wage laws as applicable.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.
#RPO, #GREEN
