Senior Software Engineer II (Java and Python) at Optum India

About the job

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, regression risk, and release rework
• Raise team delivery capability through reusable automation, clearer specifications, stronger engineering standards, mentoring, and measurable continuous improvement
• Improve production outcomes through stronger telemetry, faster diagnosis and recovery, and continuous feedback from operations into future design and delivery
• 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 professional experience
• 10+ years of professional software engineering experience delivering production solutions with increasing responsibility across the full development lifecycle
• Experience designing and delivering APIs, services, distributed systems, data integrations, or full-stack applications using sound architecture and design principles
• Demonstrated experience with automated testing across multiple test levels and with integrating tests, quality gates, and security checks into CI/CD pipelines
• Experience with cloud platforms, containers, relational and NoSQL data stores, source control, build and deployment automation, observability, and production support
• Practical experience using AI-assisted software engineering tools for one or more lifecycle activities, with the ability to evaluate and validate generated output
• Solid understanding of secure development, privacy, regulatory or policy controls, and responsible use of AI in enterprise environments
• Hands-on proficiency in at least one modern programming language and its ecosystem, such as Java, Python, C#, JavaScript, or TypeScript
• Proven ability to operate independently on complex work, influence technical decisions, communicate tradeoffs clearly, mentor peers, and collaborate across global, cross-functional teams

Preferred Qualifications

• Relevant cloud, security, software engineering, testing, or AI certifications
• Experience with specification-driven development, agentic engineering workflows, retrieval-augmented generation, LLM integration, model or agent evaluation, or production AI/ML capabilities
• Experience using tools such as GitHub Copilot, Codex, Claude Code, or equivalent AI engineering assistants in governed enterprise workflows
• Experience with performance engineering, resilience testing, contract testing, service virtualization, test-data management, or AI-assisted quality engineering
• Experience measuring delivery effectiveness through engineering metrics such as lead time, deployment frequency, change failure rate, recovery time, test effectiveness, defect escape rate, and developer experience

Success Measures

• Shorter idea-to-production cycle time for complex, high-value work
• Increased frequency of production-ready increments without compromising security, compliance or stability
• Greater automated test depth and coverage, with fewer escaped defects and less release rework
• Higher reuse of automation, specifications, engineering patterns and test assets across teams
• Improved production reliability, faster diagnosis and recovery, and stronger operational feedback into design
• Measurable uplift in team engineering standards and delivery capability through mentoring and reusable practice

Candidate Profile

You are a pragmatic engineer who combines strong software fundamentals with an automation-first and AI-first mindset. You are comfortable moving from business context to implementation and operations, and you treat testing as a core engineering responsibility. You know when AI can accelerate work and when deeper human judgment is required. You communicate clearly, learn continuously, and help others adopt practices that improve speed, quality, and customer outcomes.