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 12 Incubate 12 Implement lifecycle with appropriate controls, measurement, and production readiness.
What you19ll 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 stakeholders14balancing 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 12 Incubate 12 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 you19ll 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
Bachelor19s or Master19s 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.
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.
