Engineering leader focused on AI tooling, platform infrastructure, and scaling teams that ship.
Engineering Manager at Arcade.dev, leading the Tools and Growth teams. Arcade is building the platform that lets AI agents actually do things — tackling auth, permissions, security, and connecting to the systems we've spent decades building. I lead the teams responsible for what those tools are, and how developers discover and use them.
At Arcade, I'm focused on the infrastructure that makes AI agents useful in the real world. Not just chat — actual tool use with real auth, real permissions, and real security. The Tools team builds what agents can do; the Growth team makes sure developers can find and use it.
Previously at A Place for Mom, I led the team behind Grace — a production multi-agent system powering an AI-native senior care marketplace. Key architectural decisions: hexagonal architecture for LLM provider flexibility, composable prompt packs with filesystem fallback, and a dual-layer guardrail system combining pattern matching with LLM evaluation.
MCP server that takes a product requirements doc and decomposes it into Jira-ready epics and stories. Built with OpenAI Agents SDK. The part I'm most interested in is the eval suite — how do you measure whether a PRD was "correctly" decomposed? That's a harder problem than the decomposition itself.
5+ years of engineering leadership, Manager → Sr Manager → Director across MarTech, Platform, and AI teams. Hired and developed 15+ engineers from IC to Principal level.
Arcade.dev — First Engineering Manager. Leading Tools and Growth teams, building the platform that connects AI agents to real-world systems with proper auth, permissions, and security.
A Place for Mom — Director of Software Engineering. Led 12 engineers across AI Product and B2B Platform. Presented product strategy to CEO and executive leadership. Transformed a 21-person platform org: KTLO from 45% to 19%, P0 incidents from ~1/week to 1/year, delivery lead time from 20+ days to 6 days. Rebuilt the leadership team across an 18-month transformation — hired a QA Manager, Sr Manager, 2 Principals, 2 Staff Engineers, and promoted from within.
Realtor.com — Manager → Sr Manager across three teams that grew to 18 engineers (Auth, Identity, Notifications, MarTech). Founded the MarTech org from zero. Scaled Notifications from a single-use system to a company-wide platform doing 4B+ quarterly. Led Auth0 migration of 170M user records with zero downtime. Partnered with Data Science to productionalize ML-powered recommendation models.
Pandora — Part of a 4-engineer founding team that built the company's distributed message bus from scratch on Kafka. It became the foundation for all future data platform work.
Lightspeed Venture Partners — Founded a VC-backed startup through their fellowship. Didn't make it, but learned when to build vs. buy and how to validate before you've built anything.
Okta — SDR → AE Emerging → AE Corporate. Deliberately left engineering for enterprise sales to understand how customers evaluate, buy, and adopt platforms. Presidents Club 2019. Came back to engineering and it changed how I build — for adoption, not just technical elegance.
Technical depth: AI/ML infrastructure (multi-agent systems, agentic AI, reinforcement learning), distributed systems (Kafka, microservices), platform engineering (Auth, Identity, Notifications, Search). Computer Engineering, Santa Clara University (Dean's Scholar).
How to make AI agents actually useful beyond chat — the auth, permissions, and security problems nobody wants to solve. How to make agent evaluation rigorous when outputs are non-deterministic. The bottleneck shift nobody's talking about: AI tooling made our engineers 3-5x faster, but the product/design process couldn't feed them fast enough — so we had to rethink the entire development workflow, not just the engineering part. Whether the current multi-agent pattern is actually the right long-term abstraction or just the best one we have right now.