← cresta / Forward Deployed Product Manager, AI Agent
tailored_resume_v2 / art_-dthjq_pCXU
role
model
anthropic/claude-sonnet-4.6
created
2026-05-24T21:44
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What changed for cresta
| change | why it matters |
|---|---|
| Summary rewritten to lead with 'Forward Deployed Product Manager and AI Agent builder' identity | JD's role title and first requirement is owning AI Agent deployment lifecycle; candidate's OpenClaw and Fintellect agents are the strongest proof points |
| Summary embeds 'deployment lifecycle,' 'post-go-live optimization,' 'conversational AI agents,' 'cross-functional,' and '675M+ engagements' | JD key phrases; metrics anchor enterprise credibility for senior customer stakeholder conversations |
| Streamio AI lead bullet reframed to foreground OpenClaw multi-agent orchestration and 'full agent deployment lifecycle from use-case scoping through post-go-live optimization' | JD's primary responsibility is owning AI Agent deployment lifecycle; OpenClaw is the strongest direct proof point |
| Streamio AI second bullet reframed to emphasize 'defining agent workflows, success criteria, and automation opportunities end-to-end' | JD explicitly calls out 'agent use cases, workflows, and success criteria' and 'additional automation opportunities' |
| Streamio AI third bullet reframed to emphasize customer discovery, iterative use-case refinement, and 'turning ambiguity into execution' | JD values 'ambiguity into execution' and customer-facing product leadership; mirrors JD language accurately |
| Fintellect AI lead bullet reframed to emphasize 'domain-scoped conversational AI agents' delivering 'context-aware advisory interactions' | Cresta's core product is conversational AI agents; Fintellect agents directly parallel the product category |
| Intuit lead bullet kept as 675M+ engagements metric | Enterprise scale is the strongest credibility signal for senior customer stakeholder relationships at Fortune 500 accounts like United Airlines and Marriott |
| Splunk Scheduler Service bullet reframed to add 'bias for action in a high-growth SaaS environment' | JD bonus point explicitly calls out high-growth SaaS; 4-month delivery exemplifies the bias for action the JD requires |
| aeval project moved to lead the Projects section | Agent evaluation and post-go-live optimization is a core JD responsibility; aeval's adversarial safety testing and CI/CD safety gates directly map to optimizing deployed AI agents |
| aeval project bullet reframed to add 'directly applicable to post-go-live AI agent optimization' | Makes the relevance explicit for a hiring manager scanning quickly; JD owns post-go-live optimization |
| AutoEval bullet reframed to emphasize 72-hour to 4-minute reduction and structured evaluation outputs | Demonstrates hands-on AI agent evaluation tooling and measurable ROI — both JD priorities |
| IBM and BofA roles condensed to single bullets each | Low relevance to this role; kept for completeness and career narrative continuity per anti-pattern rules |
| Deep Learning Education Platform project removed from Projects section | Least relevant to forward deployed AI agent role; space optimization for 2-page target without cutting any roles |
JD analysis (20 key phrases)
Key phrases: AI Agent outcomesdeployment lifecyclepre-sale scopingpost-go-live optimizationforward deployedcustomer relationshipsautomation opportunitiesdesign sessions with customer stakeholdersagent use cases, workflows, and success criteriaLLM-first productscross-functionalbias for actionambiguity into executioncustomer ROIconversational AI agentsreal-time augmentationcontact centerplaybooks for agent developmenttight feedback loophigh-growth SaaS
Hard requirements:
- 5+ years in Technology Consulting, Implementations, Product, or Customer Success
- Experience with AI or LLM-first products
- Hands-on builder with bias for action
- Experience leading complex deployments
- Owning customer relationships including senior stakeholders
- Cross-functional collaboration
- Strong technical understanding and systems thinking
- Exposure to building Agents (conversational or otherwise)
- Comfortable with ambiguity in dynamic startup environments
- Passion for AI, automation, and customer experience
Preferred qualifications:
- Background in product management, conversation design, or forward deployed engineering
- Experience in startup or high-growth SaaS environment
- Familiarity with customer support and contact center operations
Per-role mapping (11 roles scored)
| role | score | reframe angle | JD phrases that map |
|---|---|---|---|
| Streamio AI — Founder & CEO | 5/5 | AI Agent builder and deployer — emphasize multi-agent orchestration, domain-scoped agents, customer discovery, and production deployment lifecycle | AI Agent outcomes, deployment lifecycle, agent use cases, workflows, automation opportunities, bias for action, ambiguity into execution, LLM-first products |
| Fintellect AI — Founder & CEO | 4/5 | Conversational AI agent deployment with LLM orchestration and customer-facing advisory interactions | conversational AI agents, agent use cases, LLM-first products, customer ROI, automation opportunities |
| Intuit — Staff Product Manager | 4/5 | Enterprise platform PM with cross-functional leadership, data-driven prioritization, and scaled deployment experience | deployment lifecycle, cross-functional, customer relationships, post-go-live optimization, playbooks |
| Splunk — Senior Product Manager | 3/5 | Enterprise SaaS PM with fast delivery cycles and customer-facing performance optimization | cross-functional, customer relationships, bias for action, high-growth SaaS |
| Kaiser Permanente — SOA Technical PM | 2/5 | Enterprise platform deployment at scale with internal customer management | deployment lifecycle, customer relationships |
| IBM — Software Engineer | 2/5 | Customer-facing technical problem solving under pressure | customer relationships, cross-functional |
| Bank of America Merrill Lynch — Tech MBA Associate | 1/5 | Quantitative analysis in enterprise context | — |
| RL Workbench | 3/5 | Hands-on AI/ML builder with deep LLM post-training knowledge | LLM-first products, bias for action |
| aeval — AI Model Evaluation Platform | 3/5 | AI quality and safety tooling — relevant to agent testing and optimization loops | post-go-live optimization, tight feedback loop |
| AutoEval — Automated Visual Evaluation | 3/5 | AI agent evaluation and optimization — directly maps to post-go-live optimization of AI agents | post-go-live optimization, AI Agent outcomes, customer ROI |
| BRAIN — Protein Structure Prediction | 2/5 | Deep AI/ML credibility | LLM-first products |
Tailored summary
Forward Deployed Product Manager and AI Agent builder with 12+ years shipping enterprise platforms and 2+ years deploying production multi-agent systems across real estate, financial, and insurance verticals. Built OpenClaw multi-agent orchestration framework and domain-scoped conversational AI agents from 0-to-1 — owning the full deployment lifecycle from use-case design through post-go-live optimization. Scaled Intuit's platform to 675M+ engagements across Fortune 500 products; led complex cross-functional deployments with senior customer stakeholders. NeurIPS published AI researcher; UC Berkeley Engineering.