Senior Software Engineer
Role details
Job location
Tech stack
Job description
- 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/Swagger
- Ensure 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 loops
- Partner with data engineering teams to produce AI features through feature stores, batch/stream ingestion, automated data quality checks, and model monitoring/drift detection
- DevOps, 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 management
- Implement 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 deployments
- Optimize AI model and pipeline performance including batch vs real-time execution, GPU/CPU right-sizing, token/throughput budgeting, cost controls, and vector index tuning
- Establish observability using Splunk and Azure Monitor/App Insights with SLOs, error budgets, dashboards, on-call runbooks, and post-mortems
- Monitor deployed AI systems for accuracy, reliability, drift, and business impact using feedback loops, A/B testing, and SLA-aligned alerts
- Security, Compliance & AI Governance
- Ensure secure, ethical, and compliant AI solutions through data minimization, PII/PHI handling, prompt/response safety, auditability, and model risk reviews
- Implement cloud security best practices including Azure Key Vault, Managed Identities, network isolation (VNet/Private Endpoints), OAuth2/OIDC, TLS, static code analysis, and container image scanning
- Architecture, Collaboration & Leadership
- Collaborate with stakeholders to translate business needs into AI-driven technical solutions with measurable outcomes across quality, cost, and delivery speed
- Lead experimentation and prototyping of emerging AI technologies and convert successful POCs into hardened, observable production services
- Document architectures, workflows, and best practices; mentor engineers in AI-first design and modern DevSecOps practices
- Tools & Developer Productivity (AI in the Loop)
- Use AI copilots and assistants such as GitHub Copilot and M365 Copilot responsibly to accelerate development, testing, documentation, and operations
- Leverage 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 services
- Work with a modern tool chain including GitHub, JetBrains IDEs, Docker, Sonar, AKS, Redis, MySQL, Snowflake, Kafka, and Splunk, using templates and golden paths to boost productivity
You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear directions on what it takes to succeed in your role as well as provide development for other roles you may be interested in.
Requirements
- 5+ years of experience designing and implementing Java/Spring Boot applications
- 5+ years of experience working with diverse data stores including relational, non-relational, JSON, and columnar databases
- 4+ years of Agile delivery experience using tools such as Rally or JIRA
- 2+ years of DevOps automation experience including Infrastructure-as-Code with Terraform
- 1+ years of hands-on experience with Azure AI services and resources such as AKS, App Services, and MLOps platforms
- 1+ years of solid experience building, updating and maintaining high-throughput RESTful APIs
Preferred Qualifications:
- Bachelor's Degree in Engineering or equivalent practical experience
- Experience collaborating with Dev/Ops teams in Agile onshore/offshore delivery models
- Experience with healthcare industry standards such as HL7, FHIR, or SMART
- Proven excellent communication skills with the ability to tell clear data and capability stories and articulate value to customers
*All Telecommuters will be required to adhere to UnitedHealth Group's Telecommuter Policy.
Benefits & conditions
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 $91,700 to $163,700 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.