Senior Software Engineer

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 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.

Optum Insight partners with payers, providers, governments and life sciences companies to simplify and enhance clinical, administrative and financial processes through software-enabled services and analytics, while advancing value-based care. Our differentiated products, technology insights, clinical expertise and analytics support the entire health system
- ultimately delivering better experiences for consumers.

Optum Insight Technology and Engineering is a critical function in Optum Insight driving the innovation and value we provide our customers and partners. This team focuses on products, solutions, platform / enabling capability development, product development lifecycle, engineering excellence and connectivity to Optum Technology.

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 for a minimum of four days per week.

Primary

Responsibilities:

Platform & APIs (AI‑First Java/Spring on Azure)Design, develop, and deploy scalable AI‑infused services using Java/Spring Boot on Azure App Services and API Management, embedding LLM, NLP, and predictive capabilities (RAG, classification, summarization, anomaly detection)Integrate ML/NLP and predictive analytics into existing systems using event‑driven architectures, vector search, caching, and idempotent REST APIs documented with OpenAPI/SwaggerEnsure performance and reliability at scale including TPS capacity planning, P95/P99 latency targets, rate limiting, resilience patterns (retry, circuit breaker), pagination/versioning, and success/error analytics (HTTP 2xx/5xx)Data, Streaming & Storage (AI‑Aware)Design and optimize MySQL schemas; leverage Redis for low‑latency features, feature flags, and inference caching; use Snowflake for analytics and feature pipelines; Kafka for event sourcing, schema registry, and model feedback loopsPartner with data engineering teams to produce AI features through feature stores, batch/stream ingestion, automated data quality checks, and model monitoring/drift detectionDevOps, MLOps & SRE (AI‑Augmented)Build and maintain enterprise‑grade AI platforms on Azure using Functions, AKS, and Workflows with Docker, including ingress, autoscaling, health probes, and secret managementImplement CI/CD and Infrastructure‑as‑Code using GitHub Actions, trunk‑based development, semantic versioning, Terraform modules/workspaces, and policy‑as‑code with blue/green and canary deploymentsOptimize AI model and pipeline performance including batch vs real‑time execution, GPU/CPU right‑sizing, token/throughput budgeting, cost controls, and vector index tuningEstablish observability using Splunk and Azure Monitor/App Insights with SLOs, error budgets, dashboards, on‑call runbooks, and post‑mortemsMonitor deployed AI systems for accuracy, reliability, drift, and business impact using feedback loops, A/B testing, and SLA‑aligned alertsSecurity, Compliance & AI GovernanceEnsure secure, ethical, and compliant AI solutions through data minimization, PII/PHI handling, prompt/response safety, auditability, and model risk reviewsImplement cloud security best practices including Azure Key Vault, Managed Identities, network isolation (VNet/Private Endpoints), OAuth2/OIDC, TLS, static code analysis, and container image scanningArchitecture, Collaboration & LeadershipCollaborate with stakeholders to translate business needs into AI‑driven technical solutions with measurable outcomes across quality, cost, and delivery speedLead experimentation and prototyping of emerging AI technologies and convert successful POCs into hardened, observable production servicesDocument architectures, workflows, and best practices; mentor engineers in AI‑first design and modern DevSecOps practicesTools & Developer Productivity (AI in the Loop)Use AI copilots and assistants such as GitHub Copilot and M Copilot responsibly to accelerate development, testing, documentation, and operationsLeverage AI and automation tools including AccelQ, StoryCraft, Dialogflow CX, Hubot, JAX, RapidAI Processing Platform, Tesseract OCR, Azure AI/Azure OpenAI, and other compliant cloud AI servicesWork with a modern tool chain including GitHub, JetBrains IDEs, Docker, S