cover_letter / art_ZOfbUDDqZBk

Dear SnapLogic Hiring Team, SnapLogic is doing something genuinely important: building the integration fabric that lets enterprises move at the speed of AI rather than being held back by legacy plumbing. The shift from deterministic pipelines to agentic, generative workflows is not incremental — it changes what a platform must be at its core. That shift is exactly where my background sits. After spending the last three years building AI-native platforms from scratch — including a multi-agent orchestration framework, a real-time RL post-training workbench, and a developer-facing infrastructure platform that scaled to 675M+ engagements — I am drawn to SnapLogic's Senior Platform PM role because it asks for the same combination of deep technical credibility, platform-as-a-product thinking, and enterprise-grade execution that has defined my career. **Technical and Platform Foundation** My platform instincts were shaped at Intuit, where I owned Developer Frameworks and Platform Infrastructure across QuickBooks, TurboTax, Mint, Mailchimp, and Credit Karma. The work was infrastructure-as-a-product in practice: I delivered the ICE Self-Service platform — DevPortal, GitOps configuration, and ICE Playground — reducing developer onboarding from 2–3 weeks to minutes in pre-production and under 24 hours for production, while mitigating over $1M in projected opex growth. I drove a rSocket migration that scaled throughput from 6K to 50K TPS, supporting approximately 1.5M concurrent connections at sub-25ms TP99. I also initiated an MSaaS Drift Detection and Resolution program, writing a Java JAR library to scan Git repositories for configuration drift and building a remediation roadmap using OpenRewrite — exactly the kind of control-plane governance work SnapLogic's platform requires. On the observability side, I worked closely with telemetry and usage data in SQL and BigQuery to surface developer pain points across roughly 20 mobile apps and 30+ product SKUs. I built Asterias, a declarative asset lifecycle management platform with a GraphQL API, and led the Mailchimp GCP-to-AWS migration for MSaaS — experience directly relevant to SnapLogic's multi-cloud and hybrid deployment challenges. My AI infrastructure depth is hands-on and recent. I built an RL post-training workbench that benchmarks GRPO, DPO, and 10 additional algorithms (PPO, DAPO, REINFORCE, REINFORCE++, RLOO, SimPO, IPO, KTO, ORPO, SPPO) across TRL, VeRL, OpenRLHF, and NeMo RL, with live SSE metric streaming, GPU Docker passthrough, and standardized throughput/memory/convergence benchmarking. I built the OpenClaw multi-agent orchestration framework with a gateway protocol, subagent delegation, profile management, and session switching — a production implementation of the agentic patterns SnapLogic is now embedding into its platform. I also built aeval, a local-first model evaluation platform with a FastAPI orchestrator, TimescaleDB, Redis job queue, and automated safety gates — infrastructure that had to be reliable, observable, and developer-friendly by design. **Why This Role** My career arc has moved steadily toward the intersection of platform infrastructure and AI-native product development. The SnapLogic platform PM role is a direct expression of that arc: it asks for someone who can own both the control plane and the data plane, partner with an AI PM to translate agentic roadmap requirements into underlying platform capabilities, and hold the platform to an enterprise-grade reliability bar — all while treating internal teams and external developers as first-class customers. That is precisely the work I have been doing, at scale and from scratch. What specifically draws me to this role is the challenge of making generative integration reliable at enterprise scale. Building MCP server infrastructure, ensuring AI agents run predictably across cloud, hybrid, and on-premises deployments, and driving the observability layer that makes the whole system debuggable — these are hard, consequential problems. SnapLogic's position as the platform on which agentic workflows run for Global 2000 customers makes the reliability and governance requirements as demanding as they get. **Selected Relevant Experience** - **ICE Platform Scaling (Intuit):** Achieved 275% YoY growth in ICE engagements, scaling to 675M+ in FY23; drove rSocket migration from 6K to 50K TPS supporting ~1.5M concurrent connections at sub-25ms TP99. - **Developer Onboarding Reduction (Intuit):** Delivered ICE Self-Service platform reducing onboarding from 2–3 weeks to minutes in pre-prod and <24 hours for production, mitigating $1M+ in projected opex growth. - **SDK and Developer Tooling (Intuit):** Extended Java and Python SDK Starter Kits with scaffolding templates, build configurations (Gradle/Maven), testing frameworks, and CI/CD integration — enabling developers to go from zero to production-ready microservice in minutes. - **Configuration Governance (Intuit):** Initiated MSaaS Drift Detection and Resolution program: wrote Java JAR library to scan Git repos for configuration drift, partnered with Design on DevPortal UI, and built remediation roadmap using OpenRewrite. - **Search Platform Ownership (Splunk):** Owned Search Service (Go microservices), Search Catalog (PostgreSQL metadata service), and SPL/SPL2; delivered Scheduler Service end-to-end in ~4 months and achieved up to 10x query performance improvements for beta enterprise customer. - **Multi-Agent Orchestration (StreamIO AI):** Implemented OpenClaw multi-agent orchestration framework with gateway protocol, subagent delegation, profile management, and session switching — enabling coordinated AI agent workflows across multiple industry verticals. - **Agentic Infrastructure with MCP (StreamIO AI):** Shipped MCP server exposing screen capture tools to AI coding assistants; integrated Claude MCP SDK for real-time contextual AI analysis — direct experience with the MCP protocol layer SnapLogic is now building on. - **Enterprise-Scale Logging Platform (Kaiser Permanente):** Led development and enterprise rollout of Splunk Logging-as-a-Service handling 1.7 TB daily volume across 200+ internal enterprise customers, including Redis-based caching for scalability and fault tolerance. **Closing** SnapLogic's mission — integrating AI, data, applications, and microservices so enterprises can build faster and smarter — is the right problem to be working on right now. The platform that makes agentic integration possible has to be as reliable and observable as the most demanding enterprise infrastructure, and it has to evolve fast enough to keep pace with a generative AI roadmap that is moving quickly. I have spent my career building exactly that kind of platform, and I would welcome the opportunity to bring that experience to SnapLogic. Thank you for your consideration. --- **O. Felix Amoruwa** famoruwa@berkeley.edu | 909-731-9011 | felixamoruwa.info