← plaid / Staff Product Manager - AI Foundations
tailored_resume_v2 / art_qrHQAIDuozI
role
model
anthropic/claude-sonnet-4.6
created
2026-05-27T23:23
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What changed for plaid
| change | why it matters |
|---|---|
| Fintellect AI moved to lead the Experience section, ahead of Streamio AI | Fintellect directly maps to Plaid's core JD requirements: RAG/embeddings, multi-LLM orchestration, applied AI in fintech/regulated domain — the single strongest proof point cluster for this role |
| Summary rewritten to lead with 'scalable AI platforms and developer infrastructure' and embed 'data and intelligence layer,' 'APIs that deliver intelligence at scale,' 'agentic systems in production,' and 'responsible AI infrastructure' | JD's first sentence defines the role as building the data and intelligence layer; summary must mirror this framing immediately |
| Fintellect bullets reframed to foreground 'data and intelligence layer,' 'embeddings,' 'regulated high-trust domain,' and 'concept-to-production' | These are the JD's exact hard requirements and key phrases; Fintellect's RAG/ChromaDB/multi-LLM stack is the strongest direct match |
| Streamio AI reframed to lead with OpenClaw multi-agent orchestration as 'agentic systems in production' | JD lists 'hands-on experience with LLMs, embeddings, or agentic systems in production' as a preferred qualification; OpenClaw is the clearest proof point |
| Intuit bullets reordered to lead with 675M+ engagements scale metric | JD values 'APIs that deliver intelligence at scale' and platform mindset — enterprise scale metric belongs in first bullet per anti-pattern rule |
| aeval moved to lead the Projects section | aeval's responsible AI, safety testing, and model evaluation pipeline directly addresses JD's 'model training and evaluation pipelines' and 'responsible AI' requirements — stronger fit than RL Workbench as lead |
| Splunk title reframed to 'Search Orchestration & Data Intelligence' | Emphasizes data platform angle relevant to Plaid's AI Foundations team without misrepresenting the role |
| Kaiser Permanente condensed to 2 bullets emphasizing regulated domain and data-at-scale | Lower relevance role; 'regulated or high-trust domains' is a JD qualifier — one proof point preserved, space conserved |
| Bank of America bullet reframed to emphasize Monte Carlo simulation in regulated financial services context | JD values 'familiarity with applied AI in regulated or high-trust domains such as fintech' — quantitative methods in financial services is the relevant angle |
| Deep Learning Education Platform project removed from Projects section | Lowest relevance to this role; space optimization for 2-page target without cutting any role entirely |
| Lawrence Berkeley National Laboratory entry folded into BRAIN project bullet | Space optimization; the NeurIPS credential carries more weight and subsumes the LBNL context |
JD analysis (18 key phrases)
Key phrases: AI Foundationsdata and intelligence layerscalable AI systemsmodel training and evaluation pipelinesAPIs that deliver intelligence at scaleresponsible AIplatform mindsetembeddings and representation learningdeveloper and consumer outcomesopen financefinancial freedommodel performance dataagentic systems in productionregulated or high-trust domainsconcept to productioncross-functional collaborationmeasurement and iterationAI platform vision
Hard requirements:
- 8+ years PM experience leading AI/ML or applied intelligence products
- Deep technical fluency in ML training, evaluation, deployment, monitoring
- Platform mindset: scalable developer/data platforms, APIs, primitives, frameworks
- Strategic and executional range: long-term AI vision + near-term experiments/launches
- Strong communicator translating complex AI for technical and non-technical audiences
- Commitment to responsible AI and transparency in regulated/high-trust domains
Preferred qualifications:
- Hands-on experience with LLMs, embeddings, or agentic systems in production
- Familiarity with applied AI in fintech, identity, or risk
Per-role mapping (7 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Streamio AI — Founder & CEO | 4/5 | LLM/agentic systems in production; 0-to-1 AI product execution; responsible AI in high-trust financial domains | agentic systems in production, concept to production, APIs that deliver intelligence at scale, LLMs |
| Fintellect AI — Founder & CEO | 5/5 | Applied AI in fintech; embeddings/RAG; multi-LLM orchestration; financial intelligence layer | data and intelligence layer, embeddings and representation learning, agentic systems in production, regulated or high-trust domains, financial freedom, developer and consumer outcomes |
| Intuit — Staff Product Manager | 5/5 | Scalable AI/developer platform at enterprise scale; APIs and primitives; platform mindset; data-driven prioritization | scalable AI systems, APIs that deliver intelligence at scale, platform mindset, developer and consumer outcomes, model performance data, cross-functional collaboration |
| Splunk — Senior Product Manager | 3/5 | Data platform and search intelligence; performance optimization; structured prioritization | scalable AI systems, measurement and iteration, model performance data |
| Kaiser Permanente — SOA Technical PM | 2/5 | Platform infrastructure in regulated domain; data at scale | regulated or high-trust domains, scalable AI systems |
| IBM — Software Engineer | 1/5 | Engineering depth | — |
| Bank of America Merrill Lynch — Tech MBA Associate | 2/5 | Quantitative methods in financial services | regulated or high-trust domains |
Tailored summary
Staff PM with 12+ years building scalable AI platforms and developer infrastructure — from architecting RAG retrieval pipelines and multi-LLM orchestration in production fintech applications to scaling a developer platform to 675M+ engagements at Intuit. Deep technical fluency across model training, evaluation pipelines, and agentic systems in production; NeurIPS published researcher. Proven ability to define AI platform vision, ship APIs that deliver intelligence at scale, and translate complex ML concepts into trusted, responsible AI infrastructure across regulated financial domains.