Program Director – Learning & Enablement

JPMorgan Chase & Co.•Plano, TX

About The Position

We are reimagining how JPMorgan Chase’s technologists learn, adopt, and apply modern software engineering practices. Our goal is to build the firmwide discipline needed to deliver secure, resilient, high-quality software at scale. This role sits at the intersection of learning strategy, experience design, and engineering craft. It will help turn emerging AI tools into everyday practices and shape how more than 65,000 technologists build AI fluency, strengthen their engineering capabilities, adopt modern patterns and practices, and use tools such as GitHub Copilot and Claude effectively. As a Program Director – Learning & Enablement, you will serve as the firm's senior subject matter expert in software engineering learning and enablement, architecting and scaling enterprise learning strategies that advance engineering excellence, developer experience, and AI fluency. Through expertise in learning science, software engineering practices, and AI-enabled ways of working, you will influence leaders, shape firmwide standards, and drive adoption of modern engineering capabilities across more than 65,000 technologists.

Requirements

Demonstrated experience designing and scaling learning, engineering enablement, or developer experience initiatives for large technical populations.
Strong understanding of modern software engineering patterns and practices across the product and software development lifecycle, including agile and product-oriented delivery.
Model and advocate AI-enabled ways of working by leveraging modern developer tooling, AI coding assistants, and data-driven practices to improve capability development, productivity, and learning effectiveness across the ecosystem.
Hands-on proficiency with AI coding assistants and code-generation tools (e.g., GitHub Copilot, Claude), with the ability to translate that fluency into teachable skills and sound engineering judgment.
Experience building data-driven measurement frameworks that connect learning or enablement activity to behavioral or performance outcomes.
Strong written and verbal communication skills, with the ability to produce executive-level strategy documents and presentations.
Working proficiency in Python for data analysis.
Familiarity with agentic/AI-native developer tooling and workflows (e.g., CLI-based AI coding agents, Model Context Protocol integrations).

Nice To Haves

Experience with product strategy frameworks (e.g., BMAD) applied to internal tooling or programs.
Experience running or scaling a Community of Practice or similar peer-learning model.
Prior experience in vendor and platform governance for learning or engineering tooling.

Responsibilities

Serve as the firm's subject matter expert for software engineering learning and enablement, providing strategic guidance and thought leadership that shapes how technologists develop engineering capabilities, AI fluency, and modern software engineering practices across the enterprise.
Architect and shape the firm's software engineering learning and enablement strategy, influencing capability-building priorities across Lines of Business, Tech Centers, and engineering domains to advance engineering excellence, developer experience, and responsible AI adoption.
Establish the learning architecture, standards, and frameworks for modular, source-controlled learning experiences centered on the cognitive skills engineers need to adopt AI tools effectively, distributed via a pull-and-push model so Lines of Business and Tech Centers can consume content on demand.
Build learning content into firmwide development programs, including internal conferences, technical workshops, early career education, and internships, ensuring consistent development of engineering fundamentals, modern delivery practices, and AI fluency across experience levels.
Serve as a leader for the firm's software engineering and AI learning communities, cultivating peer-to-peer capability building, influencing standards and practices, and accelerating adoption of modern engineering approaches at scale.
Define firmwide measurement standards that connect learning and enablement investments to engineering telemetry, developer experience, adoption of engineering practices, productivity outcomes, and organizational capability growth, providing actionable insights for senior leadership.
Establish governance frameworks and guardrails that promote responsible adoption of AI-enabled engineering practices while maintaining alignment with firm risk, control, security, and compliance expectations.
Direct platform and vendor governance across all learning applications and tooling in scope