Staff Product Manager, Agentic AI Applications

Staff Product Manager, Agentic AI Applications

GAQ327R225

Databricks is building an Agentic Enterprise applications Platform a scalable, governed AI application platform built on Databricks that enables any internal team (GTM, Finance, HR, Legal, Product) to build production grade agentic applications in weeks, not months. 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.

As a Staff Product Manager, you will own the product strategy, roadmap, and execution for the Agentic Platform the foundational layer that every domain workspace depends on. You will work across agent runtime, MCP connectors, intelligence layer, evaluation framework, developer experience, and governance to deliver a platform that reduces time-to-production from months to weeks while maintaining enterprise grade quality, security, and reliability. You will partner closely with Application Engineering, the CIO organization, and domain teams across GTM, HR, Finance, and Product to ensure the platform serves real needs and scales with the organization.

• Own the Agentic Platform strategy and roadmap. Define what ships, in what order, and why. Translate organizational outcomes into concrete platform capabilities with measurable success criteria.
• Define and drive the agent and runtime. Establish the managed agent runtime supporting multi step orchestration with durable execution, model gateway abstraction across all providers, governed tool invocation, and configurable per-agent guardrails (cost ceilings, timeouts, blast radius limits).
• Build the MCP connector ecosystem. Own the strategy for standardized, bidirectional connectors various systems of record. Drive on behalf of identity propagation, idempotency, dry-run/preview mode, and a connector SDK that lets domain teams onboard new systems without platform changes.
• Establish the intelligence layer. Define the three layer data architecture: knowledge graph (curated domain knowledge), context graph (live entity state from systems of record), and temporal memory (session, user preferences, and episodic history). Ensure unified retrieval across vector, structured, and graph sources with source traceability on every context element.
• Ship the evaluation and quality framework. Own the AI-judge evaluation pipeline: offline eval with golden datasets, online LLM-as-judge scoring, domain-specific judges (Finance, HR, Legal, Sales), and mandatory evaluation gates in CI/CD. No agent reaches production without passing quality and safety thresholds.
• Design the developer experience. Make the platform self-service by construction. Domain teams provision agent projects, promote across environments, and access connectors without platform-team tickets. SDK, CLI, sandbox environments, agent templates, and documentation — the paved road must be faster than building bespoke. Target: idea to production in <4 weeks for a standard agent.
• Define the federation and adoption model. Establish the three tiers of adoption (platform built, domain built on platform, citizen developer edge apps) with governance checkpoints at each gate. Drive the gold-standard promotion pipeline from edge prototype to production hardened service.

• 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 you understand the difference between a platform and an application, and you have opinions about API design, developer ergonomics, and self service.
• Demonstrated experience with AI/ML platforms, agent frameworks, LLM-powered applications, or agentic systems. You know what an agent runtime is, what RAG means in practice, and why evaluation is the hardest part.
• Strong technical foundation you can read architecture diagrams, discuss trade offs with engineers (e.g., sync vs. async, checkpointing strategies, context window management), and make informed prioritization decisions on deeply technical work.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Zone 1 Pay Range $172,200—$236,850 USD

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Local Pay Range $172,200—$258,400 USD

• Experience with Databricks, Lakehouse architecture, Unity Catalog, MLflow, or Delta Lake.

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.