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Databricks

Staff Product Manager, Agentic AI Applications

Databricks
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staff full-time $172,200-$258,400 USD Mountain View, California; San Francisco, California, California

First indexed 24 Jun 2026

Description

Databricks is building an Agentic Enterprise applications Platform, a scalable, governed AI application platform. As a Staff Product Manager, you will own the product strategy, roadmap, and execution for the Agentic Platform.

The platform provides a managed agent runtime, standardized MCP connectors to enterprise systems of record, a shared UI component library with a design system, an intelligence data/context layer, and a gold-standard promotion pipeline from prototype to production.

Your responsibilities:

  • Own the Agentic Platform strategy and roadmap
  • Define and drive the agent and runtime
  • Build the MCP connector ecosystem
  • Establish the intelligence layer
  • Ship the evaluation and quality framework
  • Design the developer experience
  • Define the federation and adoption model

Requirements:

  • 8+ years of product management experience, with at least 3 years on internal platform, infrastructure, or developer-experience products
  • Deep experience building platforms that other teams build on
  • Demonstrated experience with AI/ML platforms, agent frameworks, LLM-powered applications, or agentic systems
  • Strong technical foundation
  • Experience defining and shipping developer experiences
  • Proven ability to lead cross-functional initiatives across 4+ teams
  • Strong written communication
  • Comfort with ambiguity

Nice to have:

  • Experience with Databricks, Lakehouse architecture, Unity Catalog, MLflow, or Delta Lake
  • Familiarity with LangGraph, LangChain, or similar agent orchestration frameworks
  • Familiarity with MCP, A2A, or AG-UI protocols
  • Experience building AI evaluation frameworks
  • Experience with design systems, component libraries, or frontend platform work
  • Background in enterprise SaaS platform consolidation or migration
This listing is enriched and indexed by YubHub. To apply, use the employer's original posting: https://job-boards.greenhouse.io/databricks/jobs/8560772002