Senior Manager Software Engineering

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 Sr Mgr, Software Engineering to lead DMS engineering teams in OGA India through the shift from traditional software delivery to an AI-Driven Development Life Cycle (AIDLC). This leader will combine people leadership, hands-on engineering depth, and end-to-end delivery accountability to embed governed AI into discovery, specification, architecture, development, testing, release readiness, operations, and continuous improvement. The role will accelerate strategic roadmap capabilities by reducing handoffs and rework, increasing delivery throughput, strengthening quality and production stability, and building durable AI engineering capability across the team.

Primary Responsibilities:

• Lead AI-driven software delivery: establish AIDLC practices across portfolio discovery, inception, construction, readiness, and operations, and ensure AI accelerates execution while engineers retain accountability for design trade-offs, validation, risk acceptance, and outcomes
• Accelerate the roadmap: translate strategic priorities into clear specifications, prototypes, buildable and testable work units, and production increments, and remove delivery constraints and shorten time from opportunity to measurable value
• Strengthen engineering excellence: set standards for architecture, secure coding, code review, observability, CI/CD, infrastructure as code, operational readiness, documentation, reliability, and cost-conscious engineering across modern cloud-native platforms
• Manage outcomes with evidence: establish baseline and ongoing measures for lead time, throughput, deployment frequency, escaped defects, change failure rate, recovery time, automated test coverage, reliability, adoption, and capacity trends, and use the data to inspect, adapt, and demonstrate business impact
• Develop engineering talent: hire, coach, and grow software and AI engineering talent, raise proficiency in specifications, prompting, agentic workflows, architecture, testing, DevSecOps, and production ownership, and create reusable practices that improve delivery capabilities beyond a single team
• Lead cross-functional execution: partner with product, architecture, quality, security, data, infrastructure, operations, and business stakeholders to align priorities, resolve dependencies, manage risks, and maintain end-to-end ownership
• Champion reuse and continuous improvement: promote reusable specifications, prompts, skills, agents, test assets, platform components, and engineering patterns to reduce duplicate work and compound delivery gains across OGA
• Quality Engineering and Testing
  • Build quality into the lifecycle: define a shift-left testing strategy spanning unit, integration, API, contract, UI, regression, accessibility, performance, resilience, security, data, and production validation, and make testability, traceability, and release evidence part of architecture and delivery decisions
  • Scale AI-assisted testing: lead adoption of AI-generated, human-reviewed test plans, test data, automation, coverage-gap analysis, defect triage, and regression suites to improve defect prevention, release confidence, and feedback speed
  • Set organization-standard quality gates and release-evidence requirements, and hold teams accountable for functional and non-functional coverage across performance, security, resilience, accessibility, and data quality
  • Use quality signals such as escaped defects, change failure rate, and recovery time to drive continuous improvement rather than treating testing as a final gate
• AI-Driven Development Expectations
  • Establish AIDLC as the team's standard way of working and remove barriers to safe, effective adoption
  • Require use of only enterprise-approved AI tools and enforce security, privacy, compliance, auditability and intellectual-property standards for all AI-assisted work
  • Require human review of AI-generated outputs and establish clear controls for AI incidents and exceptions
  • Set expectations and measures for AI-assisted delivery, and coach engineers in specifications, prompting, agentic workflows and validation
  • Champion reusable prompts, skills, agents and patterns so responsible AI practice compounds across teams
• Expected Impact
  • Accelerate delivery of strategic capabilities by converting roadmap priorities into well-specified, testable, production-ready increments with fewer handoffs and faster feedback
  • Improve team delivery capability by embedding AI-assisted engineering, testing, automation, observability, and operational ownership into everyday work
  • Increase release confidence and production stability through earlier validation, stronger automated evidence, reduced defect leakage, and faster detection and recovery
  • Create sustained capacity by reducing repetitive work, reusing proven engineering assets, and redirecting engineers toward architecture, complex problem-solving, and customer value
  • Build a scalable AI-first engineering culture in OGA India with measurable improvements in throughput, quality, reliability, and time to value
• 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, a related technical discipline, or equivalent professional experience
• 12+ years of software engineering experience, including 5+ years leading engineering teams, managers, or complex multi-team delivery
• Experience delivering enterprise software across the full development lifecycle, from discovery and architecture through development, testing, deployment, operations, and continuous improvement
• Experience with public cloud platforms, distributed systems, APIs, microservices or event-driven architectures, containers, Kubernetes, and modern relational and NoSQL data stores such as Oracle, PostgreSQL, and MongoDB
• Experience implementing DevSecOps, CI/CD, observability, reliability, incident management, and secure software development practices in regulated or enterprise environments
• Hands-on understanding of AI-assisted software engineering, including coding agents or copilots, specification-driven development, prompt and context engineering, AI-generated tests, code review, and human validation of AI outputs
• Proven solid engineering depth in one or more modern languages such as Java, Python, JavaScript/TypeScript, C#, or Go, with experience guiding architecture and code quality across multiple technology stacks
• Proven solid people leadership, executive communication, stakeholder management, mentoring, and change leadership skills
• Proven ability to lead testing strategy and automation across functional and non-functional quality domains, including CI/CD quality gates, test data, coverage, performance, security, resilience, and production validation
• Demonstrated ability to define delivery metrics, use evidence to improve flow and quality, communicate business impact, and remove organizational barriers

Preferred Qualifications:

• Relevant cloud, architecture, security, agile, quality engineering, or AI/ML certifications
• Experience leading an enterprise AIDLC, AI-first engineering, or agentic software delivery transformation
• Experience designing or deploying AI/ML-enabled products, large language model integrations, retrieval-augmented generation, agentic workflows, model evaluation, or AI safety controls
• Experience with GitHub-based engineering workflows and AI coding assistants, plus reusable skills, plugins, or agent frameworks
• Experience leading geographically distributed teams and collaborating across India, APAC, and global stakeholders
• Experience delivering healthcare, financial services, or other highly regulated technology solutions

Success Measures

• Faster delivery of strategic roadmap capabilities with fewer handoffs and faster feedback
• Improved team delivery capability through embedded AI-assisted engineering, testing and operations
• Higher release confidence and production stability, with reduced defect leakage and faster recovery
• Sustained capacity created by reusing engineering assets and redirecting engineers to higher-value work
• A measurable, scalable AI-first engineering culture with gains in throughput, quality, reliability and time to value

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.