Lead, AI Delivery Transformation - Cary, United States
MetLife · New York, United States · 1mo ago
Join MetLife’s Global Technology and Operations (GTO) team, where what you build truly matters. Here, innovation and collaboration drive everything we do— from developing best-in-class digital solutions to safeguarding customer and employee data. Whether you're transforming insights into customer-first strategies, advocating for people in life’s most important moments, or simplifying how we serve millions worldwide, your expertise makes a lasting impact. We bring MetLife’s purpose to life, shaping a more confident future. Let’s build it together.
Role Value Proposition
As Lead, AI-First Delivery Practice, you will turn AI delivery standards into working practice in the field. You run pilots, build the context libraries and agentic patterns that make AI output reliable, and enable teams hands-on across the agentic SDLC toolchain. This is a specialist individual-contributor role focused on practice design and reusable enablement, not long-term embedded coaching. Success shows up as adoption, reusable assets, and measurable delivery improvement.
About the team: DST is an individual-contributor team inside MetLife Global Technology, working directly with CIO organizations, Enterprise DevSecOps, Platform Engineering, and Enterprise Architecture. GitHub Enterprise and the agentic SDLC toolchain are being made productive now, and the AI-First Operating Model is being designed and proven in live delivery — this role shapes it rather than inherits it.
·What is changing: Global Technology is moving to an AI-first delivery model — agents performing delivery work alongside people, specifications and curated context replacing tribal knowledge, and review, traceability, and control points rebuilt around output that people and agents produce together.
·Why this role matters: the work of this team will redefine how teams plan, build, review, govern, and deliver technology in an AI-first environment. This role proves that change in live delivery — establishing the practices, patterns, and evidence that show what works, and turning them into assets teams can pick up and run without you.
• Work AI-first in your own practice and be the visible proof of it. Teams adopt what they see working — your use of AI to build enablement assets, run analysis, and produce working prototypes is itself part of the enablement.
• Run pilot and pathfinder delivery with CIO organizations and delivery teams moving to AI-first — setting entry and exit criteria up front, capturing friction from the field, and calling the point at which a pattern is ready to scale or should be stopped.
• Build and maintain the context libraries and Copilot / agentic patterns that improve AI output quality — curating what pilots produce into governed, versioned assets, and routing field evidence to the standards and operating-model owners.
• Establish spec-driven development practice — the specification, plan, and task artifacts that give agents reliable context — and set the review standards that make agent output traceable back to intent.
• Author the enablement assets teams work from without you — playbooks, role guides, worked examples, and troubleshooting references — and enable teams directly across GitHub Enterprise, work management, and CI/CD through working sessions, pairing on live work, and time-boxed office hours, so teams reach independence rather than standing support.
• Work directly with Enterprise DevSecOps, Platform Engineering, and Enterprise Architecture on the gaps pilots expose — toolchain limitations, integration breaks, licensing and permissioning constraints, and standards conflicts — so platform capability and delivery practice advance together.
• Instrument pilots for measurement — flow and engineering-effectiveness metrics alongside AI-specific indicators such as rework ratio, review burden, and verification overhead — so adoption claims rest on evidence.
• Scale adoption beyond direct contact — build and run a champion and practitioner network, create clear enablement messaging, and partner with Learning & Development on role-based learning pathways and content.
• Hands-on experience with agentic AI frameworks and coding agents — GitHub Copilot, Copilot Coding Agent, Claude Code, or comparable — covering context and prompt engineering, tool and MCP integration, multi-agent orchestration, permissioning, output validation, human review patterns, and quality guardrails.
• Working experience with spec-driven development or comparable structured-context practices that make agent output predictable, reviewable, and traceable to intent, applied in real delivery and documented as reusable patterns.
• 8+ years in software engineering or technical product roles, with credibility earned through direct participation in software delivery, DevSecOps, platform enablement, or developer experience — and the ability to speak credibly to both engineering practitioners and senior technology leaders.
• Working command of the enterprise delivery toolchain these practices run on — GitHub Enterprise, CI/CD pipelines, pull request and branching strategy, and work-item traceability — including what it takes to make them interoperate in a governed environment.
• Experience designing and running pilots that produce decisions — setting entry and exit criteria, instrumenting them with frameworks such as DORA, SPACE, or DX Core 4, and stopping approaches that do not hold up rather than carrying them forward.
- Practitioner-level knowledge of flow-based delivery systems and modern Agile ways of working — Scaled Agile (SAFe) or comparable frameworks, the product operating model, Lean and Kanban — and of the end-to-end PDLC and SDLC.
- Practical adoption and readiness experience — champion networks, role-based enablement, stakeholder engagement, and partnership with Learning & Development to scale practice beyond direct contact.
- Experience introducing new engineering practice into a regulated delivery environment and evaluating AI output quality at scale through test harnesses, benchmarks, or review sampling.
- Experience working in a large enterprise organization of 1,000+ technologists and in regulated environments.
- Relevant certifications — SAFe, Scrum, Kanban, PMI-ACP, GitHub Copilot, Microsoft Azure AI, or comparable — and a bachelor’s degree in a related field, or equivalent practical experience.
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The expected salary range for this position is $130,000 - $160,000. This role may also be eligible for annual short-term incentive compensation. All incentives and benefits are subject to the applicable plan terms.
Benefits We OfferOur U.S. benefits address holistic well-being with programs for physical and mental health, financial wellness, and support for families. We offer a comprehensive health plan that includes medical/prescription drug and vision, dental insurance, and no-cost short- and long-term disability. We also provide company-paid life insurance and legal services, a retirement pension funded entirely by MetLife and 401(k) with employer matching, group discounts on voluntary insurance products including auto and home, pet, critical illness, hospital indemnity, and accident insurance, as well as Employee Assistance Program (EAP) and digital mental health programs, parental leave, paid time off, paid holidays, volunteer time off, tuition assistance and much more! For more information regarding MetLife’s U.S. benefits, please click here.
Recognized on Fortune magazine's list of the "World's Most Admired Companies", Fortune World’s 25 Best Workplaces™, as well as the Fortune 100 Best Companies to Work For®, MetLife, through its subsidiaries and affiliates, is one of the world’s leading financial services companies; providing insurance, annuities, employee benefits and asset management to individual and institutional customers. With operations in more than 40 markets, we hold leading positions in the United States, Latin America, Asia, Europe, and the Middle East.
As part of our New Frontier strategy, MetLife is building an AI-enabled, people-centered future. We’re looking for people who bring curiosity, adaptability, and a growth mindset as we use AI to enhance how we serve customers, support communities, and evolve the way work gets done. At MetLife, AI is a responsible partner that supports human judgment, creativity, and continuous improvement while helping us build trust, inclusion, and long-term value.
Our purpose is simple - to help our colleagues, customers, communities, and the world at large create a more confident future. United by purpose and guided by our core values - Win Together, Do the Right Thing, Deliver Impact Over Activity, and Think Ahead - we’re inspired to transform the next century in financial services. At MetLife, it’s #AllTogetherPossible. Join us!
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