DevGen.AI Lead Product Engineer-Executive Director

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management, and wealth management services. The Firm’s employees serve clients worldwide, including corporations, governments, and individuals, from more than 1,200 offices in 43 countries.

DevGenAI has been an extraordinary AI development platform built internally within Morgan Stanley. With a CIO 100 Award (2026), Banking Award Finalist (2026), and winner of the Banking Award (2025) recognition, DevGenAI is well known and highly regarded.

Our Global Head of Technology openly promoted DevGenAI on LinkedIn. The DevGenAI core team has received more than 9 U.S. patents in the last two years, with more than 8 team members becoming first-time patent recipients. The platform enables the development of tech-for-tech solutions, Strats solutions, and business solutions (e.g., an HR solution currently in the POC/Dev stage). With more than 200 engineers contributing code and more than 4,000 PRs per year, it is one of the most active inner-source platforms and contributes extensively to various production solutions currently used by our highly accomplished “User Partners.”

Interested in joining a team that’s eager to create, innovate, and make an impact on the world? Read on.

The DevGen.AI Lead Product Engineer will lead product execution, engineering enablement, and customer adoption for DevGen.AI, Morgan Stanley’s enterprise capability for turning AI experimentation into governed, reusable, production-ready business impact.

The role sits at the intersection of customers, platform engineering, governance, InnerSource contributors, and divisional stakeholders. It is responsible for connecting enterprise demand to DevGen.AI capabilities, shaping reusable patterns and agents, accelerating high-value use cases, and ensuring teams can move through the Innovate → Incubate → Implement lifecycle with appropriate controls, measurement, and production readiness.

What you’ll do in the role:

• Lead DevGen.AI product execution across platform capabilities, reusable agents, prompts, patterns, accelerators, APIs, and adoption workflows.
• Partner with engineering, architecture, governance, SRE, security, and divisional teams to move use cases from experimentation to pilot validation and enterprise-scale implementation.
• Serve as the connective tissue across product, engineering, users, governance, and executive stakeholders—balancing speed, reuse, safety, and measurable business impact
• Own intake orchestration for high-value DevGen.AI demand, ensuring teams are guided to the right capabilities, reusable assets, and delivery path.
• Translate customer demand, usage data, and recurring enterprise needs into prioritized product backlog themes and platform enhancement opportunities.
• Drive adoption of the Innovate → Incubate → Implement lifecycle, including feasibility validation, pilot measurement, governance gates, production readiness, and scalable launch patterns.
• Build and guide rapid prototypes, POCs, reusable reference implementations, technical playbooks, and patterns that reduce time-to-value for delivery teams.
• Enable responsible AI adoption by coordinating with governance stakeholders and embedding completeness, accuracy, timeliness, controls, and measurement into delivery practices.
• Champion InnerSource contribution practices so reusable assets, prompts, agents, rubrics, and implementation patterns become firmwide capabilities rather than one-off solutions.
• Lead community enablement through office hours, demos, onboarding support, technical guidance, documentation, and knowledge-sharing forums.
• Identify opportunities to reduce duplication across teams by connecting similar use cases, promoting common patterns, and scaling best-of-breed implementations.
• Track, communicate, and improve adoption, productivity, ROI, contribution, and platform impact metrics for stakeholders and senior leadership

What you’ll bring to the role:

• Strong product engineering background with proven experience delivering enterprise platforms, developer tools, AI/LLM applications, or internal technology products.
• Hands-on understanding of Generative AI, LLMs, prompt engineering, agentic architectures, RAG patterns, evaluation methods, and responsible AI delivery practices.
• Ability to translate complex customer needs into reusable platform capabilities, product backlog priorities, technical patter…