Lead, AI Delivery Transformation

MetLife; Cary (NC); On-site; USD 140,000 - 180,000; Full time

Job summary

MetLife’s Global Technology and Operations team in Cary, NC seeks a Lead, AI-First Delivery Practice to translate AI standards into practical, scalable delivery. You will run pilots, build reusable enablement assets, and guide teams across the agentic SDLC toolchain to improve reliability and velocity.

This specialist individual-contributor role focuses on practice design and measurable adoption, working with CIO organizations, DevSecOps, and Platform Engineering to embody an AI-first operating

Qualifications

• Hands-on experience with agentic AI frameworks and coding agents, covering context and prompt engineering, tool integration, multi-agent orchestration, permissioning, output validation, and human-review patterns.
• Working experience with spec-driven development or comparable structured-context practices that make agent output predictable and traceable.
• 8+ years in software engineering or technical product roles with delivery experience in DevSecOps or platform enablement.
• Familiarity with enterprise delivery toolchains such as GitHub Enterprise, CI/CD pipelines, PR/branching strategies, and work-item traceability.
• Experience designing and running pilots with measurable criteria and ability to stop approaches that do not hold up.

Responsibilities

• Work AI-first in your own practice and demonstrate it; your enablement assets and prototypes serve as evidence.
• Run pilot and pathfinder delivery with CIO organizations and delivery teams moving to AI-first, defining entry/exit criteria and capturing friction.
• Build and maintain context libraries and Copilot/agentic patterns to improve output quality and governance.
• Establish spec-driven development practice with artifacts that guide agents and ensure traceability to intent.
• Author enablement assets (playbooks, role guides, worked examples) and enable teams across GitHub Enterprise, work management, and CI/CD.
• Collaborate with Enterprise DevSecOps, Platform Engineering, and Enterprise Architecture to advance platform capability and delivery practice.
• Instrument pilots with metrics (flow, engineering effectiveness, rework, review burden, verification overhead).
• Scale adoption by building champion networks and partnering with Learning & Development for role-based paths.

Skills

Agentic AI frameworks

Prompt engineering

Context libraries

Multi-agent orchestration

CI/CD tooling

Education

Bachelor's degree in CS or related field

Tools

GitHub Copilot

GitHub Enterprise

Copilot Coding Agent

Claude Code

Job description

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.

The Opportunity

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.

Role Value Proposition

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.

Key Responsibilities

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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