Lead Product Manager, AI Platform

Disney Disney · Media · Bay Lake, FL +1

Lead Product Manager for an internal AI Platform, focusing on building and scaling AI capabilities. The role involves owning the end-to-end product lifecycle from stakeholder needs to shipped AI features, including scoping, building agents and retrieval pipelines, driving technical direction, and establishing evaluation patterns. Requires strong technical and product skills, ability to operate independently, and influence across teams.

What you'd actually do

  1. Take internal stakeholder asks from initial conversation to shipped AI capability: owning discovery, scoping, design, build, and rollout end-to-end, without waiting for requirements to be handed down.
  2. Build AI-native workflows on the internal AI Platform stack: prompts, agents, retrieval pipelines, evaluation harnesses, and the integrations that make them useful.
  3. Drive technical direction for the Product Engineering pod within the AI Platform team: setting prototyping standards, evaluation patterns, and shared tooling that the rest of the team builds on.
  4. Operate independently of the standard sprint cycle; make product and architecture decisions in a low-structure environment, knowing when to cut scope, when to ship, and when to ask for input.
  5. Share work-in-progress at 70% complete to gather stakeholder feedback and iterate, rather than holding work until it's polished.

Skills

Required

  • Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent years of work experience.
  • 7+ years of combined experience across software engineering and product roles in technical or AI-platform contexts.
  • Production experience with LLM-powered applications, including advanced prompt engineering, agent frameworks, evaluation pipelines, and retrieval-augmented generation.
  • Hands-on technical capability sufficient to scope, build, and ship a working prototype end-to-end – comfortable in Python and modern AI tooling, able to operate without engineering or PM handoff.
  • Demonstrated experience taking ambiguous stakeholder asks and returning working systems, comfortable talking to users directly, without waiting for a product manager to define requirements.
  • Demonstrated history of taking products or capabilities from 0 → 1 inside an enterprise or platform environment, with measurable adoption outcomes.
  • Hands-on production experience with at least one LLM gateway (LiteLLM, OpenRouter, Bedrock, or equivalent), one workflow or agent runtime (n8n, LangGraph, Temporal, or equivalent), and one evaluation or observability framework (Arize, Phoenix, Langfuse, or equivalent).
  • Demonstrated judgment in scope, speed, and quality tradeoffs: knowing when to ship at 70% to learn fast, when to harden, and when to retire work that isn't paying off.
  • Track record of driving cross-team outcomes through influence without direct authority: coordinating with PMs, platform engineers, and adjacent engineering teams.
  • Excellent communication skills, including the ability to translate AI capability into business outcomes for senior stakeholders.

Nice to have

  • Background as a forward-deployed engineer, applied AI engineer, or product-minded engineer in an AI-native company or AI platform context.
  • Direct production experience with MCP (Model Context Protocol), OpenWebUI, n8n, LiteLLM or similar tools within this space.
  • History shipping deliberately scrappy prototypes for stakeholder learning, with the judgment to distinguish prototype scale from production scale.
  • Familiarity with enterprise security, compliance, and governance patterns for AI systems.
  • Experience instrumenting AI products with eval frameworks and AI observability tooling.
  • Background partnering with non-technical senior leaders to decompose business problems into shipped AI capabilities.

What the JD emphasized

  • own the path from initial stakeholder conversation through working system — scoping, building, evaluating, and graduating or retiring AI work end-to-end
  • operate independently of the standard sprint cycle
  • work directly with internal stakeholders without a PM intermediary
  • influence adjacent engineers and PMs without managing them
  • Production experience with LLM-powered applications, including advanced prompt engineering, agent frameworks, evaluation pipelines, and retrieval-augmented generation.
  • Hands-on technical capability sufficient to scope, build, and ship a working prototype end-to-end – comfortable in Python and modern AI tooling, able to operate without engineering or PM handoff.
  • Demonstrated experience taking ambiguous stakeholder asks and returning working systems, comfortable talking to users directly, without waiting for a product manager to define requirements.
  • Demonstrated history of taking products or capabilities from 0 → 1 inside an enterprise or platform environment, with measurable adoption outcomes.
  • Demonstrated judgment in scope, speed, and quality tradeoffs: knowing when to ship at 70% to learn fast, when to harden, and when to retire work that isn't paying off.
  • Track record of driving cross-team outcomes through influence without direct authority: coordinating with PMs, platform engineers, and adjacent engineering teams.

Other signals

  • building and scaling the internal AI Platform
  • translating internal stakeholder needs into shipped AI capabilities
  • own the path from initial stakeholder conversation through working system — scoping, building, evaluating, and graduating or retiring AI work end-to-end
  • set the technical direction for how the team prototypes and ships
  • operate independently of the standard sprint cycle
  • work directly with internal stakeholders without a PM intermediary
  • influence adjacent engineers and PMs without managing them
  • mentorship of adjacent engineers and PMs is expected