Staff Product Manager, Agentic AI Applications
Databricks is hiring a Staff Product Manager, Agentic AI Applications in Mountain View and San Francisco, United States. It pays $172k-$237k a year and Level rates it ; you can apply on Databricks.
Level's read on this job
- On Level's scale, this AI product manager role is Builds AI ●●●●: the job is building AI systems.
- Pay on this posting starts in line with the typical AI product manager posting on Level.
- The posting mentions LangChain, LangGraph and MLflow among its tools and skills.
- Level classes it as a senior role.
- Databricks has 894 other open roles on Level.
AI in this role
Lead product strategy and execution for an enterprise agentic AI application platform and runtime.
Location: San Francisco or Mountain View, California
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.
The impact you will have:
- 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.
What we look for:
- 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.
- Experience defining and shipping developer experiences: SDKs, CLIs, templates, documentation, and self service workflows. You measure success by adoption and developer NPS, not feature count.
- Proven ability to lead cross-functional initiatives across 4+ teams without direct authority. You influence through clarity, conviction, and stakeholder alignment.
- Strong written communication strategy documents, PRDs, and executive briefs that drive alignment at VP and CIO level.
- Comfort with ambiguity, you will define the roadmap for capabilities that don't exist yet, in a space that is evolving weekly.
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 (Model Context Protocol), A2A (Agent-to-Agent), or AG-UI protocols.
- Experience building AI evaluation frameworks LLM-as-judge, red-teaming, or automated quality scoring.
- Experience with design systems, component libraries, or frontend platform work.
- Background in enterprise SaaS platform consolidation or migration.
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
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
Benefits
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.
How we rate this
Staff Product Manager, Agentic AI Applications at Databricks rates 80 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in AI product manager jobs at this level
Pay
- This job starts at
- $172k
- Typical AI product manager job
- $180k
This posting starts close to the typical AI product manager job. See AI product manager jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide that one model's output is better than another's for a given task?
- Tell me about a project where product management was part of your work. What did you do?
- Tell me about a project where agentic ai was part of your work. What did you do?
- Tell me about a project where ai platforms was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: RAG, AI Evaluation, Product Management, Agentic AI, and AI Platforms. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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