• Design, build, and operate enterprise AI systems across our client portfolio. • Work end-to-end across the AI stack — from inference engines and platform infrastructure up through application-level engineering. • Lead end-to-end design, build, and operation of AI systems on AI Factory platforms across multiple client engagements. • Engineer and tune LLM inference serving stacks — primary depth in vLLM with breadth across the inference ecosystem — for client latency, throughput, and cost targets. • Tune inference performance through KV cache management, paged attention, batching strategies, and Dynamo-based disaggregated serving. • Architect and operate MLOps pipelines covering model lifecycle, registries, deployment, rollback, and observability. • Design and engineer RAG applications on top of vector databases. • Build and tune prompt-engineering patterns at production scale. • Engineer high-performance storage and networking for AI workloads. • Operate Kubernetes clusters underpinning AI workloads. • Build and maintain container images, registries, and CI/CD pipelines for AI/ML services. • Implement monitoring, alerting, logging, and capacity planning across the AI stack. • Harden environments to meet client security and compliance requirements. • Lead troubleshooting across various environments and technologies. • Engage directly with client stakeholders — technical and executive — to communicate status, root cause, options, and recommendations. • Mentor and code-review work from less senior engineers; raise the technical bar of every engagement you join. • Author runbooks, reference architectures, and knowledge base content; lead client knowledge transfer and enablement sessions. • Participate in on-call rotation and incident response for production AI workloads. • Contribute reusable patterns, tooling, and reference designs back to the practice.

• Design and develop agent components and templates for the Collaboratory • Lead small-scale implementation projects, coordinating with stakeholders and team members • Create and maintain standardized documentation and “recipe cards” for AI solutions • Conduct quality assurance testing and debugging of agent components • Analyze usage metrics and user feedback to improve solution offerings • Facilitate user training sessions and support enablement events • Maintain version control documentation and update logs for Collaboratory items • Assist in packaging common support requests into reusable agent components • Support junior analysts and contribute to team knowledge sharing

Requisition Number: 2349023 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, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. The AI/ML Engineering team within Optum Health Claims, part of Optum Technology, is redefining how healthcare claims are processed at massive scale. This team sits at the intersection of claims platforms, data engineering, observability, and applied AI, driving the transition from manual, rules-driven operations to intelligent, touchless, and self-healing claims systems. As Director of AI/ML Engineering, you will lead a transformation agenda, growing, high-impact team responsible for building enterprise-grade AI platforms, claims data pipelines, and operational intelligence capabilities that support tens of millions of claims annually. Your charter spans traditional ML, modern AI techniques, and automation, applied directly to real operational problems-reducing rework, improving accuracy, lowering SG&A, and accelerating outcomes. You will partner closely with claims engineering, product, data, operations, enterprise architecture, and business leaders across Optum and UnitedHealth Group. The work is hands-on, highly visible, and outcome-driven-ideal for a leader excited to build AI as a fabric across healthcare operations, not just models in isolation. The team operates at enterprise scale, with strong executive sponsorship, mature production platforms, and a mandate to innovate. If you are energized by turning complex data and workflows into measurable operational advantage, this role offers the opportunity to lead meaningful transformation in one of the most critical domains in healthcare. 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. Primary Responsibilities: - Lead the design and execution of AI/ML engineering strategy for claims platforms, aligned with business outcomes - Build and operationalize enterprise data pipelines across multiple claims systems - Develop and scale observability and operational intelligence suites that surface manual work, rework, exceptions, and automation opportunities - Apply traditional ML, advanced AI, and intelligent automation to reduce manual claims processing and improve accuracy - Partner with engineering, product, data, and operations leaders to embed AI directly into end-to-end claims workflows - Establish engineering standards, MLOps practices, and responsible AI patterns for production systems - Mentor and grow senior engineers and AI practitioners in a high-expectation, high-impact environment 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: - 6+ years of hands on experience with traditional ML techniques (e.g., regression, tree based models, NLP, classical optimization) and modern AI approaches (deep learning, GenAI/LLMs, RAG, agentic AI systems) - 5+ years of deep experience in data engineering, including data quality management, feature pipelines, analytical pipelines, and ML data readiness at scale - 5+ years of experience leading and scaling engineering teams (typically 10-40+ engineers/data scientists) delivering production AI/ML systems with clear ownership of reliability, cost, performance, and business outcomes - 4+ years of proven ability to translate complex operational problems into production grade, scalable AI/ML solutions embedded in enterprise workflows - 3+ years of solid, hands on experience with claims platforms or core transaction systems, with healthcare claims domain experience (e.g., adjudication, EDI, pricing, edits, payment integrity, post pay analytics) Preferred Qualifications: - Experience applying AI/ML to large scale operational workflows (claims, payments, servicing, or similar domains) - Experience building AI solutions with governance, compliance, and reliability in mind - Familiarity with cloud based AI/ML platforms and enterprise integration patterns - Proven exposure to observability, process mining, or operational analytics platforms - Proven solid executive communication skills with a track record of delivering measurable business impact *All employees working remotely 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 $134,600 to $230,800 annually based on full-time employment. We comply with all minimum wage laws as applicable. 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. At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission. UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations. UnitedHealth Group is a drug - free workplace. Candidates are required to pass a drug test before beginning employment. #BI-Hybrid

Role Description This is a mid-level AI Engineer role on the core product team, focused on building agentic systems that automate complex, multi-step workflows across regulated and enterprise domains. You'll work across the full stack to ship production LLM-based services, ensure reliability and safety, and collaborate with leadership, product, and design to deliver measurable user impact. What You'll Do - Design, build, and maintain agentic systems that automate complex, multi-step workflows across healthcare, legal, fintech, logistics, and compliance domains. - Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure including vector databases, embeddings, and indexing for domain-specific search at scale. - Implement multi-agent orchestration, tool-calling, memory, and reasoning components to deliver robust AI-driven user experiences. - Develop evaluation and safety infrastructure to measure model performance, surface regressions, and enforce enterprise-level trust and reliability. - Ship full-stack AI products from MVP to enterprise-grade by designing APIs and data models, implementing frontend and backend code, and operating production systems with CI/CD, monitoring, and testing. - Collaborate with leadership, product, and design to prioritize work, define success metrics, and iterate based on user feedback and telemetry. Qualifications - 2–8 years of software engineering experience with demonstrated delivery of shipped user-facing or backend products. - Practical experience deploying LLMs or LLM-based services in production, including prompt design, orchestration, and tool integration. - Proficiency across the stack: Python plus TypeScript/React (or equivalent), and experience with cloud platforms (AWS or GCP) and relational or NoSQL databases. - Working knowledge of RAG patterns, vector databases, embeddings, and retrieval pipelines, with sound judgment to choose appropriate approaches. - Experience building automated tests, evaluations, and monitoring for AI systems to ensure reliability beyond demos. - Experience with agent or workflow frameworks and orchestration tools. - Familiarity with fine-tuning, parameter-efficient tuning, or multi-modal model integration. - Background building multi-tenant or enterprise-ready systems, or experience in regulated industries such as healthcare, fintech, or legal. - Experience designing API-driven, high-throughput systems and real-time product features. - Proven ownership delivering end-to-end features from data model to deploy and monitoring, with a user-centric and pragmatic engineering mindset. Location - Remote or San Francisco, CA, United States.