Senior Software Engineer - Java/.net/python/go, API development, gen AI

UnitedHealth Group; Chennai District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Optum India seeks a Software Engineer to join an AI-enabled delivery pod and drive software delivery through the AI Product Delivery Lifecycle. You will work with Product, Quality and Operations teams to realize measurable business outcomes.

As a Builder you will design, build, test, deploy, and operate software services, APIs, and data integrations, ensuring quality, security, and maintainability across Construction, Readiness, and Operations phases.

Qualifications

• Contributes to AI-enabled delivery pod with cross-functional teams.
• Designs, builds, tests, deploys software services, APIs, data integrations.
• Translates approved specs into tasks, tests, and docs using AIDLC.
• Directs enterprise AI coding agents with prompts and guardrails to generate code and tests.
• Collaborates with product, architecture, data, security and operations to clarify requirements.
• Participates in design reviews, pair programming, demos, incidents and retrospectives.
• Builds observability, deployment automation, and operational readiness into solutions.
• Owns quality and tests within the pod; ensures non-functional requirements are met.
• Shares reusable patterns and learnings from AI-driven delivery.

Responsibilities

• Owner of end-to-end outcomes in a small AI-enabled delivery pod.
• Design, build, test, deploy, operate, and improve software services, APIs, and data integrations.
• Translate approved specifications into tasks, artifacts, tests, and release-ready increments using AIDLC.
• Direct AI agents (Claude Code, Codex, Copilot) with prompts and guardrails to generate code and tests.
• Partner with product, architecture, data, security, and operations to clarify requirements and acceptance criteria.
• Participate in design/code reviews, pair programming, demonstrations, incidents, and retrospectives.
• Build observability, resilience, deployment automation, and supportability into solutions.
• Quality Engineering: own testing and validation within the pod, including AI-assisted testing.

Job 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.

We are seeking a Software Engineer to join an AI-enabled delivery pod within DMS in OGA India and help deliver software through the AI Product Delivery Lifecycle (AIDLC), Optum Technology's standard way of working in which AI drives the flow while engineers own the outcomes. You will apply strong software engineering fundamentals across the five AIDLC phases (Portfolio Discovery, Inception, Construction, Readiness, and Operations), using enterprise-approved agent harnesses, reusable Skills, and prompts from the Optum AI Catalog to produce secure, maintainable, production-ready software. As a Builder in a small, cross-functional pod of three to five people, you are accountable not only for creating software but also for validating AI-generated output, engineering quality into every increment, and converting well-defined product intent into measurable customer and business outcomes.

Primary Responsibilities:

Contribute as a Builder in a small, cross-functional, AI-enabled delivery pod that owns outcomes end to end, working closely with the Product, Quality, and Operations pod rolesDesign, build, test, deploy, operate, and continuously improve software services, APIs, applications, data integrations, and automation across the Construction, Readiness, and Operations phasesUse AIDLC and GitHub Spec-Kit spec-driven development (Specify, Plan, Tasks, Implement) to translate approved specifications into decomposed tasks, implementation artifacts, tests, documentation, and release-ready incrementsDirect enterprise-approved AI coding agents and copilots such as Claude Code, OpenAI Codex, and GitHub Copilot with clear prompts and AGENTS.md guardrails to generate and refine code and tests, and validate every output for correctness, maintainability, performance, security, accessibility, and alignment with product intentPartner with product, architecture, data, security, and operations stakeholders to clarify requirements, define acceptance criteria, identify dependencies, and resolve delivery risks earlyParticipate in design and code reviews, pair programming, frequent demonstrations, incident learning, and retrospectives, including AI retrospectives, and contribute reusable patterns, prompts, and lessons learned back to the AI commonsBuild observability, resilience, deployment automation, and supportability into solutions, and use production feedback to improve quality and delivery flowQuality Engineering and TestingOwn quality as part of software engineering work, and build and test concurrently within the pod rather than relying on downstream quality handoffsCreate, review, execute, and maintain unit, component, API, integration, contract, end-to-end, regression, and acceptance tests appropriate to the solutionDefine and validate non-functional requirements, including performance, scalability, reliability, resiliency, security, accessibility, privacy, and operabilityUse AI-assisted test generation, coverage-gap analysis, defect detection, test-data creation, and failure analysis while applying human judgment to verify scenarios, edge cases, expected results, and risk coverageIntegrate automated tests, static analysis, dependency and security scans, quality gates, and deployment checks into CI/CD pipelines so validation is continuous and release is a low-risk, evidence-based eventTrace tests and validation evidence to requirements and acceptance criteria, diagnose defects, perform root-cause analysis, and prevent recurrence through automation and engineering improvementsAI Product Delivery ExpectationsAdopt the AIDLC as your way of working by turning approved specifications and context-rich plans into incremental, validated tasks, with accountable human review of every AI-generated output before merge or releaseUse agent harnesses, Skills, prompts, and connectors from the Optum AI Catalog under UAIS enterprise governance, and use only enterprise-approved AI toolsFollow security, privacy, compliance, and responsible-AI policies, and never expose confidential, proprietary, regulated, or personal data to unapproved servicesUse AI agents for boilerplate, refactoring, test generation, documentation, and debugging, and critically evaluate every output for correctness, security, maintainability, and alignment with product intentTrack the outcomes of AI-assisted work, such as cycle time, automated coverage, and escaped defects, and raise AI .

Quality Engineering and Testing

Own quality as part of software engineering work, and build and test concurrently within the pod rather than relying on downstream quality handoffsCreate, review, execute, and maintain unit, component, API, integration, contract, end-to-end, regression, and acceptance tests appropriate to the solutionDefine and validate non-functional requirements, including performance, scalability, reliability, resiliency, security, accessibility, privacy, and operabilityUse AI-assisted test generation, coverage-gap analysis, defect detection, test-data creation, and failure analysis while applying human judgment to verify scenarios, edge cases, expected results, and risk coverageIntegrate automated tests, static analysis, dependency and security scans, quality gates, and deployment checks into CI/CD pipelines so validation is continuous and release is a low-risk, evidence-based eventTrace tests and validation evidence to requirements and acceptance criteria, diagnose defects, perform root-cause analysis, and prevent recurrence through automation and engineering improvements

AI Product Delivery Expectations

Adopt the AIDLC as your way of working by turning approved specifications and context-rich plans into incremental, validated tasks, with accountable human review of every AI-generated output before merge or releaseUse agent harnesses, Skills, prompts, and connectors from the Optum AI Catalog under UAIS enterprise governance, and use only enterprise-approved AI toolsFollow security, privacy, compliance, and responsible-AI policies, and never expose confidential, proprietary, regulated, or personal data to unapproved servicesUse AI agents for boilerplate, refactoring, test generation, documentation, and debugging, and critically evaluate every output for correctness, security, maintainability, and alignment with product intentTrack the outcomes of AI-assisted work, such as cycle time, automated coverage, and escaped defects, and raise AI .