Engineering & Scale
I build the infrastructure that makes AI actually work.
Too busy shipping to take a photo.
Will fix soon ;)
AI failures aren't model failures — they're systems failures. Authentication breaks. Databases choke. Pipelines silently drop data. I'm the one who makes sure none of that happens. I contribute to Kubernetes core, build backend systems in Go and TypeScript, and design the architecture that lets AI run reliably at scale. When something needs to work at 3am with no one watching, I built it.
AI is only as good as the systems it runs on. I build the systems that don't break.
Highlights
Expertise
APIs, data models, concurrency-safe services, auth flows. Go, PostgreSQL, Redis. The stuff that doesn't break.
End-to-end systems with React, Next.js, TypeScript. Backend-first design. Performance and correctness.
LLM-driven workflows, LangChain agents, automation pipelines. AI that runs reliably, not just impressively.
Selected Work
01Systems programming, open source, testing
Contributing to Kubernetes core repository with focus on resource quantity parsing, numeric boundary handling, overflow safety. Proposed correctness improvements, added unit tests, engaged in API-level discussions.
02Backend development, AI integration, OAuth
Built backend-driven CRM automating personalized outreach using LLM workflows. OAuth-based email access, scalable dispatch and tracking pipelines.
03Backend architecture, authentication, systems design
Designed Flask + PostgreSQL backend powering desktop analytics. Redis-backed refresh token system, session management, secure auth for Electron client.
Backend engineering, data pipelines, ETL
Built resilient data ingestion pipeline scraping multiple sources with fallback logic. Consistent data availability under unreliable upstream conditions.
Backend development, business logic
Backend services and business logic for billing, inventory, analytics. +23% service speed, –40% inventory costs.
Recognition
Currently
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