Senior Software Engineer II (Java and Python)

India | Sept. 10, 2026

Report as Closed

Company: UnitedHealth Group

Country: India

Type: Onsite

Employment: Full Time

Description:

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:

• Apply AIDLC practices to translate business intent into buildable, testable specifications, implementation plans, tasks, code, and release-ready increments
• Design, develop, test, deploy, monitor, and maintain software components and services across the team's technology stack
• Own complex components and features end to end, influencing architecture, design patterns, APIs, data models, integration approaches, and non-functional requirements
• Build secure, resilient, observable, and scalable solutions using modern programming languages, frameworks, cloud services, containers, and engineering practices relevant to the product
• Implement CI/CD automation, infrastructure and configuration practices, quality gates, telemetry, and release controls that support frequent, reliable delivery
• Diagnose complex production issues, perform root-cause analysis, improve runbooks and monitoring, and feed operational learning into backlog and design decisions
• Identify high-value automation and reuse opportunities that remove delivery bottlenecks, reduce repetitive work, and increase the team's capacity for strategic roadmap capabilities
• Participate in design reviews, code reviews, pair programming, and technical demonstrations, and mentor engineers and promote modern engineering standards
• Quality Engineering and Testing
  • Define risk-based test strategies and measurable acceptance conditions early in the lifecycle so that quality, security, performance, resilience, and operability are designed in rather than inspected at the end
  • Generate, review, and maintain automated unit, component, API, integration, contract, end-to-end, regression, performance, security, accessibility, and reliability tests appropriate to the solution
  • Use AI-assisted test generation, test-data creation, coverage-gap analysis, defect triage, and failure diagnosis while applying human review to edge cases, ambiguous requirements, and high-risk changes
  • Integrate automated tests and quality gates into CI/CD pipelines, and monitor code coverage, test effectiveness, flaky tests, escaped defects, change failure rate, and recovery signals
  • Lead root-cause analysis and preventive actions for defects, and improve testability, observability, and engineering practices to reduce rework and increase release confidence
  • Partner across product, architecture, security, operations, and quality disciplines to produce traceable evidence that each increment is ready for production
• AI-Driven Development Expectations
  • Adopt AIDLC as a way of working, not simply a tooling choice, using clear specifications, context-rich plans, incremental tasks, automated validation and accountable human review
  • Use only enterprise-approved AI coding assistants, agents and reusable skills, and follow security, privacy, compliance and responsible-AI policies, and never expose sensitive data to unapproved services
  • Move beyond using AI to building with it where it adds value, including LLM integration, retrieval-augmented generation and agentic workflows, with human validation of every output for correctness, security and maintainability
  • Measure and improve outcomes such as lead time, automation coverage, escaped defects, change failure rate, recovery time and developer experience
  • Create and share reusable prompts, skills, patterns and lessons learned so the broader team can scale safe and effective AI-assisted delivery
• Expected Impact
  • Accelerate priority roadmap delivery by reducing manual analysis, coding, testing, and documentation effort through governed AI-assisted workflows
  • Increase the frequency of production-ready increments without compromising security, compliance, stability, or maintainability
  • Improve automated test depth and coverage, shorten feedback cycles, and reduce escaped defects, re

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