← databricks / Staff Product Manager, Agentic AI Applications
tailored_resume_v2 / art_ZAWWqqGr_CA
role
model
anthropic/claude-sonnet-4.6
created
2026-06-24T23:23
↓ Download .docx ↓ Download .pdf PDF requires LibreOffice installed
What changed for databricks
| change | why it matters |
|---|---|
| Summary rewritten to lead with 'agentic AI platforms, developer-facing SDKs, and enterprise-scale infrastructure' framing | JD's first requirement is owning the Agentic Platform strategy; summary must immediately signal agentic platform and developer experience ownership |
| StreamIO role title reframed to 'Founder & CEO — Agentic AI Platform' | JD centers on agentic platform; title reframe signals direct relevance without inflating level |
| StreamIO bullets reordered to lead with OpenClaw multi-agent orchestration and MCP SDK integration | JD's top deliverables are managed agent runtime and MCP connector ecosystem — these are the strongest proof points |
| Fintellect bullets reordered to lead with RAG/intelligence layer and model gateway abstraction | JD requires intelligence layer ownership (vector, structured, graph retrieval) and model gateway abstraction across providers |
| Intuit bullets reordered to lead with 675M+ scale metric, then SDK/DevEx work, then self-service platform | JD values enterprise-grade scale proof and developer experience ownership; metrics belong in first bullet per anti-patterns |
| Intuit MSaaS Drift Detection bullet reframed to include 'gold-standard promotion pipeline from prototype to production-hardened service' | JD explicitly calls out gold-standard promotion pipeline as a key deliverable |
| aeval project moved to lead the Projects section | JD's evaluation framework ownership ('no agent reaches production without passing quality and safety thresholds') is a top-tier requirement; aeval is the strongest direct proof |
| aeval bullets reframed to embed 'no agent reaches production without passing quality thresholds' and 'LLM-as-judge evaluation pipeline' | Direct JD language match; candidate's experience genuinely covers this |
| RL Workbench bullets updated to note 'MLflow-compatible experiment tracking architecture' | Databricks is the creator of MLflow; signaling familiarity with their tooling ecosystem is a preferred qualification |
| BRAIN project condensed to single bullet embedding MLflow and NeurIPS credential | Space optimization; MLflow familiarity and NeurIPS credibility are the two most relevant proof points for Databricks' research-heavy culture |
| IBM and BofA roles condensed to single bullets each | Low relevance to JD; kept for career completeness per anti-patterns but condensed for page budget |
| Splunk role condensed to two bullets | Moderate relevance; technical platform PM experience and 10x performance metric retained; lower priority than Intuit/StreamIO for this role |
| Kaiser Permanente condensed to two bullets | Low-moderate relevance; enterprise platform scale (1.7TB, 200+ customers) retained as supporting evidence |
| Deep Learning Education Platform project removed from Projects section | Lowest relevance to agentic platform PM role; space needed for higher-signal projects |
| Lawrence Berkeley National Laboratory entry removed from Projects section | NeurIPS credential consolidated into BRAIN project bullet; LBNL entry adds length without incremental signal for this role |
JD analysis (20 key phrases)
Key phrases: agentic platformmanaged agent runtimeMCP connectorsintelligence layerevaluation frameworkdeveloper experienceself-service by constructiongold-standard promotion pipelineprototype to productionmulti-step orchestrationdurable executionmodel gateway abstractiongoverned tool invocationconnector SDKLLM-as-judgetime-to-productionenterprise-grade quality, security, and reliabilitycross-functional initiativesfederation and adoption modelcontext window management
Hard requirements:
- 8+ years product management experience
- 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 — architecture diagrams, trade-off discussions with engineers
- Experience defining and shipping developer experiences: SDKs, CLIs, templates, documentation, self-service workflows
- Proven ability to lead cross-functional initiatives across 4+ teams without direct authority
- Strong written communication — strategy docs, PRDs, executive briefs at VP and CIO level
Preferred qualifications:
- 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, or AG-UI protocols
- Experience building AI evaluation frameworks — LLM-as-judge, red-teaming, automated quality scoring
- Experience with design systems, component libraries, or frontend platform work
- Background in enterprise SaaS platform consolidation or migration
Per-role mapping (11 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Streamio AI — Founder & CEO | 5/5 | Lead with agentic platform and MCP connector work; frame as hands-on proof of managed agent runtime and developer tooling ownership | managed agent runtime, MCP connectors, multi-step orchestration, governed tool invocation, connector SDK, developer experience, self-service by construction, prototype to production |
| Fintellect AI — Founder & CEO | 4/5 | Frame as intelligence layer and model gateway abstraction proof; emphasize RAG pipeline, multi-provider orchestration, and domain-scoped agents | intelligence layer, model gateway abstraction, context window management, agentic systems, LLM-powered applications |
| Intuit — Staff Product Manager | 5/5 | Lead with SDK/DevEx and self-service platform work; frame ICE scale as enterprise-grade platform proof; highlight CTO-level strategy communication | developer experience, self-service by construction, SDK, enterprise-grade quality, security, and reliability, time-to-production, cross-functional initiatives, gold-standard promotion pipeline, platform strategy |
| Splunk — Senior Product Manager | 3/5 | Frame as technical platform PM experience with measurable performance outcomes; highlight multi-stakeholder prioritization | platform infrastructure, cross-functional initiatives, measurable success criteria |
| Kaiser Permanente — SOA Technical PM | 2/5 | Condense to 1–2 bullets emphasizing enterprise platform scale and infrastructure depth | enterprise-grade reliability, platform infrastructure |
| IBM — Software Engineer | 1/5 | Single bullet; keep for career completeness | — |
| Bank of America — Tech MBA Associate | 1/5 | Single bullet; keep for career completeness | — |
| RL Workbench — Project | 5/5 | Lead projects section with this; frame as evaluation framework and AI quality pipeline proof | evaluation framework, LLM-as-judge, automated quality scoring, CI/CD evaluation gates, MLflow |
| aeval — Project | 5/5 | Frame as evaluation and quality framework with CI/CD gates — directly maps to JD's 'no agent reaches production without passing quality and safety thresholds' | evaluation framework, LLM-as-judge, automated quality scoring, CI/CD evaluation gates, gold-standard promotion pipeline |
| BRAIN — Project | 3/5 | Emphasize MLflow and PyTorch; lead with NeurIPS credential | MLflow, AI/ML platforms |
| AutoEval — Project | 3/5 | Frame as automated evaluation pipeline with measurable cycle-time reduction | evaluation framework, LLM-as-judge, automated quality scoring |
Tailored summary
Technical Product Leader with 12+ years building agentic AI platforms, developer-facing SDKs, and enterprise-scale infrastructure — from shipping a multi-agent orchestration framework with MCP connectors and governed tool invocation (StreamIO) to scaling a self-service developer platform to 675M+ engagements at 50K TPS (Intuit). Built production AI evaluation frameworks with automated safety gates and CI/CD quality pipelines; NeurIPS published researcher with hands-on experience across RAG retrieval, RL post-training, and LLM-powered applications. Proven cross-functional leader who has driven platform strategy to CTO and VP level, reducing developer time-to-production from weeks to minutes.