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.
Primary Responsibilities:
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Design, code, test, debug, document and maintain the services that make up the AI platform and agent runtime, taking systems from design review through production operation and long-term support
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Design the public surface of the platform - APIs, SDKs, client libraries, schemas and versioning strategy - so that internal teams, client engineering teams and external builders can integrate without bespoke work for each consumer
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Build the Capability Gateway: implement Model Context Protocol (MCP) servers and tool interfaces with typed contracts, authentication and authorization, input and output validation, quota and rate limiting, audit logging, and an automated conformance test suite that new capabilities must pass
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Build and operate the agent harness and runtime: orchestration and tool-calling execution, state and session management, retrieval paths, concurrency, queuing and retries, timeout and failure handling, idempotency, and graceful degradation under partial outage
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Establish engineering standards, methods and tooling for the portfolio: coding standards, code review practice, branching and release strategy, CI/CD pipelines, infrastructure as code, environment management, and automated testing at unit, contract, integration and end-to-end levels
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Engineer the platform for cost and scale, including inference routing across models, caching, batching, connection and resource pooling, capacity planning, and per-tenant cost telemetry that ties spend to completed work
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Code and deploy the ML and LLM components of the platform into production: model and prompt serving, retrieval and embedding pipelines, adapters and fine-tuned variants, and the deployment mechanics behind them including canary, shadow and rollback paths
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Implement AI/ML capabilities in support of healthcare workflows, which may include natural language processing and understanding, semantic search, intent classification, information extraction, document AI and computer vision, and automatic speech recognition applied to clinical notes, claims, faxes, referrals, prior authorizations and recorded encounters
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Build the evaluation and experimentation infrastructure the platform depends on: golden datasets, task-level benchmarks, offline and online evals, human-in-the-loop review workflows, regression gates wired into CI, and experiment tooling with defensible statistical treatment of results
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Work with large-scale computing frameworks and data analysis systems to build the data foundation behind agent capabilities, including distributed processing, feature and vector stores, streaming ingestion, schema evolution and lineage
Engineer for PHI from the first commit: data minimization and de-identification, tenant isolation, encryption in transit and at rest, secrets management, retention and access controls, and enforcement that keeps agent behavior inside approved data and action boundaries -
Evaluate new tools, techniques and strategies - models, frameworks, orchestration approaches, serving stacks - with enough rigor to make a build, buy or adopt recommendation, and communicate the results and trade-offs to leadership and internal stakeholders
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Partner across Platform Enablement, product management, design, security, privacy and legal, forward-deployed engineering, operations and external partners such as Azure and Databricks on one integrated backlog with explicit interfaces and SLOs
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Raise the engineering bar through reference implementations, reusable libraries, design review participation, code review and technical mentorship, and influence thought and leadership on where the platform should go next
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Comply with all applicable Company policies, procedures, and business directives, changes including those relating to work location, team assignments, work schedules, and flexible work arrangements
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:
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Bachelor's degree in computer science, engineering or a related technical field, or equivalent experience
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10+ years of professional software engineering experience building and operating production systems
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5+ years of experience designing distributed services and platform-level APIs consumed by other engineering teams
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Hands-on experience with LLM-based or agentic systems, including retrieval-augmented generation, tool and function calling, and orchestration
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Expert-level proficiency in Python and at least one additional production language such as Java, TypeScript, Scala or C++
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Demonstrated depth in software engineering fundamentals: testing strategy, debugging complex production issues, performance profiling, code review and technical documentation
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Experience with cloud-native delivery, including containers and Kubernetes, CI/CD, infrastructure as code, and observability tooling
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Experience with large-scale computing frameworks and distributed data processing such as Spark/Databricks, Ray, Kafka or equivalent
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Experience building software subject to security, privacy or regulatory constraints
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Demonstrated ability to explain complex technical results and trade-offs clearly to non-technical stakeholders and senior leaders
Preferred Qualifications:
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Experience building multi-tenant platforms or services, including tenant isolation, quota and entitlement enforcement, and developer experience as an explicit product concern
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Experience implementing or operating Model Context Protocol (MCP) servers, tool-calling standards, or agent frameworks such as LangGraph or equivalent
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Experience with vector databases, embedding lifecycle management, hybrid retrieval and enterprise knowledge graphs
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Experience with inference cost management or FinOps for AI workloads
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Experience with responsible AI practice, including bias and fairness evaluation, model risk management, model documentation, or alignment to frameworks such as the NIST AI Risk Management Framework
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Experience on a 0-to-1 product or platform, working directly with design partners or customers to shape what gets built
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Experience with Azure AI services and Databricks in a regulated enterprise environment
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Health care industry experience including provider, payer, medical device, pharmaceutical, or other health services a plus
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.
