Senior Software Engineer - Java/.net/python/go, API development, gen AI - Chennai, Bangalore at UnitedHealth Group — Chennai, IND | Base Career | Base Career
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 roles
Design, build, test, deploy, operate, and continuously improve software services, APIs, applications, data integrations, and automation across the Construction, Readiness, and Operations phases
Use 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 increments
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Direct 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 intent
Partner with product, architecture, data, security, and operations stakeholders to clarify requirements, define acceptance criteria, identify dependencies, and resolve delivery risks early
Participate 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 commons
Build observability, resilience, deployment automation, and supportability into solutions, and use production feedback to improve quality and delivery flow
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 handoffs Create, review, execute, and maintain unit, component, API, integration, contract, end-to-end, regression, and acceptance tests appropriate to the solution Define and validate non-functional requirements, including performance, scalability, reliability, resiliency, security, accessibility, privacy, and operability. Use 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 coverage Integrate 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 event Trace tests and validation evidence to requirements and acceptance criteria, diagnose defects, perform root-cause analysis, and prevent recurrence through automation and engineering improvements
Own quality as part of software engineering work, and build and test concurrently within the pod rather than relying on downstream quality handoffs
Create, review, execute, and maintain unit, component, API, integration, contract, end-to-end, regression, and acceptance tests appropriate to the solution
Define and validate non-functional requirements, including performance, scalability, reliability, resiliency, security, accessibility, privacy, and operability.
Use 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 coverage
Integrate 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 event
Trace 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 release Use agent harnesses, Skills, prompts, and connectors from the Optum AI Catalog under UAIS enterprise governance, and use only enterprise-approved AI tools Follow security, privacy, compliance, and responsible-AI policies, and never expose confidential, proprietary, regulated, or personal data to unapproved services Use AI agents for boilerplate, refactoring, test generation, documentation, and debugging, and critically evaluate every output for correctness, security, maintainability, and alignment with product intent Track the outcomes of AI-assisted work, such as cycle time, automated coverage, and escaped defects, and raise AI incidents or unreliable output through the defined channels Contribute reusable prompts, patterns, and lessons learned to the AI commons so the pod can scale safe and effective AI-assisted delivery
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 release
Use agent harnesses, Skills, prompts, and connectors from the Optum AI Catalog under UAIS enterprise governance, and use only enterprise-approved AI tools
Follow security, privacy, compliance, and responsible-AI policies, and never expose confidential, proprietary, regulated, or personal data to unapproved services
Use AI agents for boilerplate, refactoring, test generation, documentation, and debugging, and critically evaluate every output for correctness, security, maintainability, and alignment with product intent
Track the outcomes of AI-assisted work, such as cycle time, automated coverage, and escaped defects, and raise AI incidents or unreliable output through the defined channels
Contribute reusable prompts, patterns, and lessons learned to the AI commons so the pod can scale safe and effective AI-assisted delivery
Expected Impact Move validated features from specification to production with fewer handoffs and less rework by working within an outcome-owning pod and automating repeatable lifecycle work Improve pod delivery capacity by embedding AI-assisted planning, coding, review, test generation, documentation, and operational readiness into everyday engineering Increase release confidence through continuous functional and non-functional validation, expanded automated coverage, earlier defect detection, and traceable evidence against acceptance criteria Strengthen engineering consistency by applying reusable Skills, prompts, architecture guardrails, security controls, and human review of AI-generated output
Move validated features from specification to production with fewer handoffs and less rework by working within an outcome-owning pod and automating repeatable lifecycle work
Improve pod delivery capacity by embedding AI-assisted planning, coding, review, test generation, documentation, and operational readiness into everyday engineering
Increase release confidence through continuous functional and non-functional validation, expanded automated coverage, earlier defect detection, and traceable evidence against acceptance criteria
Strengthen engineering consistency by applying reusable Skills, prompts, architecture guardrails, security controls, and human review of AI-generated output
Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so
Required Qualifications
Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field, or equivalent practical experience
3+ years of professional software engineering experience delivering production software
Experience with core engineering concepts including data structures, algorithms, object-oriented or functional design, APIs, relational and NoSQL databases, version control, and secure coding
Hands-on experience with automated testing across multiple test levels and with integrating validation into CI/CD workflows
Experience working across the development lifecycle from requirements and design through deployment, monitoring, support, and continuous improvement
Experience using, or a demonstrated ability to rapidly adopt, AI-assisted development tools such as agent harnesses and coding copilots (Claude Code, OpenAI Codex, or GitHub Copilot) for coding, testing, review, documentation, or troubleshooting
Proficiency in at least one modern programming language such as Java, Python, JavaScript/TypeScript, C#, or Go
Proven ability to critically evaluate AI-generated output, identify defects and unsupported assumptions, protect sensitive information, and apply human accountability before merge, promotion, or release.
Proven solid analytical, collaboration, and communication skills, with the ability to work effectively in a distributed, cross-functional pod.
Preferred Qualifications
Experience with cloud platforms, containers, Kubernetes, infrastructure as code, microservices, event-driven architecture, or distributed systems
Experience with GitHub or equivalent source control, pull-request workflows, automated build and deployment platforms, observability, and production support
Experience with specification-driven development such as GitHub Spec-Kit, acceptance-criteria design, rapid prototyping, agile delivery, or product-oriented engineering
Experience applying responsible AI, security, privacy, compliance, and governance controls to AI-enabled workflows or products
Familiarity with generative AI, large language models, prompt engineering, retrieval-augmented generation, agentic workflows, reusable Skills and plugins, or connectors such as MCP
Proven evidence of improving engineering throughput, quality, reliability, reuse, or developer experience through automation and measurable continuous improvement
Success Measures
Shorter time to build (FCT/TTB) from approved specification to validated, deployable increment
Greater automated test coverage and fewer defects discovered late in the lifecycle or after release, reflected in a lower change failure rate
Higher quality and maintainability of AI-assisted code, tests, and technical documentation
Increased reuse of approved Skills, prompts, agents, and automation from the AI commons
Improved DORA outcomes, including deployment frequency, lead time for changes, and failed deployment recovery time, with greater production stability
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.
About UnitedHealth Group
Health Insurance & Managed Care390000 employeesFounded 1977
Diversified health care company helping people live healthier lives.
