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 Lead Product Engineer will lead product execution, engineering enablement, and customer adoption for , 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 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 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 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 patterns, and implementation roadmaps. Experience leading engineering teams or cross-functional delivery across product, platform, architecture, security, SRE, governance, and business stakeholders. Strong technical fluency in APIs, cloud-native engineering, platform architecture, software delivery lifecycle, observability, access control, and production readiness practices. Demonstrated ability to build prototypes, reference implementations, technical documentation, reusable accelerators, and developer enablement materials. Excellent communication skills with the ability to engage senior stakeholders, explain technical concepts clearly, and influence without direct authority. Strong execution discipline, prioritization skills, and comfort operating in a fast-moving, high-demand environment with multiple concurrent use cases. Experience working in global, matrixed, regulated, and highly collaborative enterprise technology environments Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, Information Systems, or a related technical discipline. 10+ years of experience in software engineering, product engineering, solution architecture, AI platforms, developer platforms, or enterprise technology delivery. Prior experience leading engineers, product squads, solution engineering teams, or cross-functional execution across multiple stakeholder groups. Proven track record delivering enterprise-scale platforms or reusable technology capabilities with measurable adoption and business impact Preferred skills: Experience with frameworks and patterns such as LangChain, LangGraph, Semantic Kernel, MCP, multi-agent orchestration, vector search, and RAG pipelines. Experience with one or more - Azure OpenAI, AWS Bedrock, Google Vertex AI, internal AI gateways, or enterprise model access/control patterns. Background in platform engineering, developer experience, InnerSource/community-led development, solution architecture, or enterprise AI enablement. Familiarity with governance, model evaluation, risk controls, entitlement management, monitoring/SRE, secure architecture, and production support in regulated environments. Experience measuring business value through adoption metrics, productivity gains, usage analytics, contribution metrics, ROI, and capacity creation. WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work. To learn more about our offices across the globe, please copy and paste into your browser. Expected base pay rates for the role will be between $195,000 and $275,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs. Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents. Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences. For more information, please visit : .