cover_letter / art_fo5XxtdaGwI

Dear Robinhood TradePMR Hiring Team, Robinhood is doing something genuinely consequential with TradePMR: taking the democratization mission that reshaped retail investing and applying it to the advisor layer — the infrastructure layer that will ultimately determine how the largest wealth transfer in history actually flows. That ambition is what draws me to this role. My background sits precisely at the intersection of developer-platform product management, API and integration strategy, and applied AI — and I have spent the last several years building exactly the kind of partner-first, AI-native tooling that TradePMR is now trying to become the foundation for. **Technical and Platform Foundation** At Intuit, I owned the developer platform and framework infrastructure that powered QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma. I delivered the ICE Self-Service platform — DevPortal, GitOps config, ICE Playground — reducing developer onboarding from two to three weeks down to minutes in pre-production and under 24 hours for production, while mitigating over $1M in projected opex growth. I extended Java and Python SDK Starter Kits with scaffolding templates, build configurations (Gradle/Maven), testing frameworks, and CI/CD integration so that developers could go from zero to production-ready microservice in minutes. I scaled ICE engagements 275% year-over-year to 675M+ in FY23 and drove a throughput migration from 6K to 50K TPS via rSocket, supporting approximately 1.5M concurrent connections at sub-25ms TP99. These are not abstract platform metrics — they represent the kind of developer-experience and API-infrastructure work that makes or breaks an integration ecosystem. On the AI-native side, I built OpenClaw, a multi-agent orchestration framework with a gateway protocol, subagent delegation, profile management, and session switching — coordinating AI agent workflows across real estate, insurance, health/dental, and financial-markets verticals. I also implemented Kinde OAuth 2.0 flows, Apple Sign-In with deep-link schemes, and SSO session handling across multiple production applications, and I have direct hands-on experience reasoning through auth patterns (OAuth 2.0, MCP SDK) with partner engineering teams — exactly the technical fluency this role requires when scoping integrations with PMS/CRM vendors or evaluating emerging AI-agent standards like MCP. In my own ventures, I built Fintellect — a mobile-first AI financial-education and investing platform — architecting a RAG retrieval pipeline with multi-provider LLM orchestration (Claude, GPT-4, Gemini), fallback routing, structured-output validation, and token-budget optimization, alongside 13 specialized AI advisors delivering context-aware guidance on live Alpaca market data. I also shipped Vantage with 40+ agent tools, async job polling, idempotent endpoints, and SSE streaming — production-grade AI platform work, not prototypes. **Why This Role** TradePMR's integration platform challenge is one I find genuinely compelling: building the framework that lets growth-oriented, multicustodial RIAs plug their PMS, CRM, financial planning, and AI-native tools into a single modern custodian — and doing it in a way that is coherent, well-governed, and ready for the agentic workflows coming next. The combination of outward-facing partner ownership and internal technical product ownership is exactly the dual-mode work I have done throughout my career, and I am energized by the opportunity to define what "great" looks like as the scope comes together. **Selected Experience Directly Relevant to This Role** - **ICE Self-Service Platform (Intuit):** Delivered DevPortal, GitOps config, and ICE Playground end-to-end — reducing developer onboarding from weeks to minutes, mitigating $1M+ in projected opex, and scaling to 675M+ engagements in FY23; directly analogous to building a partner-facing integration platform with sandbox, documentation, and governance. - **SDK and Developer Tooling (Intuit):** Extended Java and Python SDK Starter Kits with scaffolding, build configs (Gradle/Maven), testing frameworks, and CI/CD integration — the same developer-experience work required to make TradePMR easy to integrate with. - **Enterprise Service Language Assessment (Intuit):** Conducted cross-functional analysis across 9 languages and 30+ product SKUs, presenting strategic investment recommendations to the CTO — demonstrates the prioritization-framework and executive-communication skills needed to tier and resource integration requests across dozens of vendors. - **OpenClaw Multi-Agent Orchestration (StreamIO AI):** Built gateway protocol, subagent delegation, and session management across multiple industry verticals — direct experience with the agentic, AI-driven advisor workflow patterns TradePMR is preparing for. - **OAuth 2.0 / Auth Architecture (StreamIO AI / Fintellect AI):** Implemented Kinde OAuth 2.0, Apple Sign-In with deep-link schemes, SSO session handling, and MCP SDK integration across multiple production apps — comfortable as primary technical contact with partner engineering teams on auth and API design. - **Fintellect AI Financial Platform:** Architected RAG pipeline with multi-provider LLM orchestration, structured-output validation, and 13 specialized AI advisors on live market data — a concrete, shipped example of AI-native fintech product work, not theoretical familiarity. - **Splunk Search Orchestration (Senior PM):** Owned Go microservices, PostgreSQL metadata service, and SPL/SPL2; delivered Scheduler Service end-to-end in four months; designed RICE-based prioritization framework across three microservice backlogs balancing internal partners, third-party developers, and Fortune 500 customers — the same multi-stakeholder balancing act required across TradePMR's vendor and advisor portfolio. **Closing** Robinhood's mission to democratize finance has always resonated with me — and TradePMR represents its most structurally important expression yet. The advisor layer is where the $124 trillion wealth transfer will actually be intermediated, and the custodian that wins will be the one that is easiest to build on, most coherent as a platform, and most prepared for the agentic workflows that are already arriving. I would bring 12+ years of developer-platform, API-integration, and applied AI product experience to that build — and I am ready to define what great looks like. Thank you for your consideration. I look forward to the conversation. Sincerely, **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info