The long version, with the footnotes.

Senior Product Engineer

Own the systems behind Shoppin's AI fashion product as it grew from a discovery/try-on app into a full commerce and custom-manufacturing platform: the feed, the try-on, the pricing, the payments, and eventually the machine that turns a generated garment into a factory-ready sewing pattern. Roughly: everything between the content firehose and a real garment on someone's doorstep.

• → The hardest part of a recommendation system is deciding what "personalized" means for a brand-new user
• → Provider racing beats picking the best provider; latency *is* the product
• → Deterministic beats clever: LLM-only pricing gave the same tee two prices; a model-backed layer fixed it
• → You don't automate the expert away; you hand them a near-perfect starting draft and let their judgment finish it
• → Redis Lua is the cheapest distributed lock you'll ever write
• → PG COPY protocol is 10–100× faster than executemany; more people should reach for it

• → ~2,090 commits across 12 repos in ~9 months: Python backends, a TS API, two Next.js apps, a React Native app, and GPU/ML serving
• → The pattern engine took a master pattern-maker from 2–4 drafts a day to 9–10
• → Built a two-stage query predictor that guesses your next search before you type it

Founding AI Engineer

Show up on day one to the brief "we need a backend" and leave with every system the product needs: architecture, schemas, real-time messaging, RAG, LLM serving, eval pipelines. The classic founding deal: own everything, hand it off as the team grows.

• → Founding-eng is a speed game: write for rewrite; nothing you ship survives year two anyway
• → Multi-provider LLM fallback isn't optional; models fail silently, and they fail often
• → RAG quality is a query-expansion problem more than it is a retrieval problem
• → One backend, four channels: the hard part is making them feel like one assistant, not four bots
• → Eval pipelines kill 80% of the "why does the bot sound dumb today" debugging

• → Scaled to 20K+ concurrent sessions on FastAPI before anyone thought to benchmark it
• → Built the whole gRPC LLM layer on a weekend because the team kept hitting rate limits
• → The LLM infra I shipped quietly became every team's default, the best kind of adoption

Founding AI Engineer

Build a freight-forwarding agent that could run a logistics desk on its own: read the emails, understand the ask, chase the missing details, get the quotes, and pick the best one, all without a human babysitting each step.

• → Real-world documents are hostile; a scanned rate sheet will break any extraction that assumes clean input
• → An agent that says "I need one more thing" beats one that guesses and gets it wrong

• → Turnaround went from hours to minutes and drove 3× outbound volume

Founding Engineer

First real founding gig: show up, learn how to ship enterprise software, and convince Toyota and UCO Bank that this is something they can run on.

• → Enterprise clients tell you the real requirements six months in, so start with the flexible version
• → A 33× latency win (1s → 30ms) is usually hiding in one query nobody dared to rewrite
• → The security work nobody wants to own (SAML, DLP, malware scanning) is what unlocks the enterprise deal
• → Azure is not AWS; both are fine, neither is magic

• → First time selling into Toyota; still fascinated by the procurement processes of companies older than me
• → Built full-text search from scratch because Elasticsearch felt like overkill for our corpus