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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Principal AI/ML Engineer: Office-Based preferred, will consider Remote - **Company:** Optum, Inc - **Location:** Eden Prairie, MN, United States (Remote available) - **Experience:** Expert - **Salary:** $184,600.0 - $316,500.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Code Review, Continuous Integration, Data Integration, Programming Tools, Information Retrieval, Python (Programming Language), Key Management, Machine Learning, Microsoft Office, Performance Tuning, Tensorflow, Software Engineering, Management of Software Versions, Google Cloud, Pytorch, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Generative AI, Agentic-AI, Build Management, AI Platforms, Scikit Learn, Kubernetes, Information Technology, Deployment Automation, Machine Learning Operations, Cloud Optimization, Software Coding, Restful APIs, Serverless Computing - **Published:** October 11, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=077d79aad8723a24 ## About the Role * Bachelor's degree in computer science, software engineering, a related technical field, or equivalent practical experience * 15+ years of relevant engineering experience, including 10+ years of hands-on software engineering and 6+ years delivering production AI/ML solutions * Hands-on depth in cloud-native architecture, automated testing, CI/CD, IaC, observability, secure design, performance tuning, and production incident resolution * Demonstrated ability to integrate and evaluate ML and LLM solutions, including generative AI, RAG, and agents, and to implement deployment, versioning, monitoring, and responsible AI controls * Demonstrated delivery of complex, production-grade distributed services, APIs, data integrations, and reusable platforms, with strong coding skills in Python and at least one additional general-purpose language * Evidence of cross-team technical leadership on ambiguous initiatives: influencing architecture and roadmaps, resolving dependencies and risks, mentoring engineers, and explaining trade-offs clearly to technical and business audiences, * Experience with healthcare applications, clinical or administrative workflows, sensitive health data, or other regulated environments * Experience with one or more major cloud platforms and managed AI services, such as Azure, Amazon Web Services, or Google Cloud * Experience with relevant tools such as PyTorch, TensorFlow, scikit-learn, agent orchestration frameworks, vector databases, containers, and Kubernetes; equivalent technologies are welcome * Experience scaling shared engineering or AI platforms across teams, conducting rigorous offline and online evaluations, or measuring the impact of AI-assisted development * All employees working remotely will be required to adhere to UnitedHealth Group's Telecommuter Policy. ## Description * Define architecture for complex initiatives spanning multiple teams. Turn business needs into technical designs, phased delivery plans, and measurable quality goals while resolving dependencies, technical risks, and competing design priorities * Design and build distributed, cloud-native services, robust APIs, reusable libraries, and shared platforms. Establish integration contracts and engineering patterns that teams can adopt across healthcare applications and workflows * Create working prototypes, evaluate design alternatives, and lead technical and code reviews. Improve testability, maintainability, and automated unit, integration, contract, and end-to-end testing * Build reliable data integrations and event-driven workflows. Address data quality, schema evolution, failure recovery, and access controls across application, enterprise, and AI data sources * Advance continuous integration and continuous delivery (CI/CD), infrastructure as code (IaC), and automated security and quality checks. Create repeatable deployment patterns with controlled releases, rollback paths, and clear operational readiness criteria * Own production readiness and ongoing technical health. Define service-level objectives, observability, alerts, and runbooks; diagnose incidents, lead root-cause analysis, and implement improvements that prevent recurrence * Optimize performance, scalability, resilience, and cloud cost. Apply secure design, least-privilege access, secrets management, and threat modeling while balancing delivery speed with long-term reliability * Influence technical roadmaps and engineering standards across GTS. Document architectural decisions, guide platform reuse, and mentor engineers through pairing, design discussions, and reviews without direct management responsibilities * Develop and integrate machine learning and large language model (LLM) solutions, including generative AI, RAG, and vector retrieval. Select approaches based on workflow needs, data readiness, accuracy, latency, cost, and operational constraints * Build AI agents with orchestration, tool calling, and guardrails. Define permission boundaries, validate tool inputs and outputs, prevent prompt injection and unintended data exposure, and provide human review for consequential actions * Establish model, prompt, and retrieval evaluations using representative datasets and clear acceptance criteria. Compare AI solutions with simpler baselines; assess factual grounding, task success, bias, safety, and failure modes before release * Implement machine learning and LLM operations (MLOps/LLMOps) pipelines. Version models, prompts, datasets, and retrieval configurations; automate deployment, monitor quality and drift, and support traceable releases and rollbacks * Guide adoption of AI-powered development tools and cloud AI services through hands-on trials and reusable patterns. Measure engineering value, validate generated code, and maintain testing, security, and intellectual property safeguards * Partner with product, data, security, and business leaders on responsible AI delivery. Protect sensitive data, assess privacy and bias risks, define human oversight and fallback behavior, and resolve cross-team risks from experimentation through production 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.