Full-Stack & AI Product Engineer
Building developer tools, real-time systems, mobile products, and practical AI workflows.
Portfolio · Repositories · Engineering activity · LinkedIn
I turn product ideas into working systems: from architecture and backend services to polished interfaces, deployment, observability, and the workflows that keep a product maintainable.
My current work sits at the intersection of:
- AI product engineering — MCP services, agent-ready tools, semantic discovery, and human-controlled AI workflows
- Full-stack systems — TypeScript, Node.js, React, Next.js, Ruby on Rails, PostgreSQL, and real-time communication
- Mobile delivery — React Native, reusable application foundations, release automation, analytics, and operations
- Infrastructure — Docker, GitHub Actions, self-hosted services, security boundaries, and production-oriented documentation
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A mobile-first system for building and evolving applications on a shared React Native and Nx foundation.
Private product · case study |
An App Store discovery engine that grows and ranks keyword branches using live metrics and LLM-assisted semantic expansion.
Private product · case study |
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A production cargo-search and spider-orchestration platform for freight-board automation, persistent search jobs, and operational monitoring.
Live product · private source |
A production multiplayer adaptation of 2048 with real-time matches, matchmaking, AI opponents, strategic attack and heal mechanics, leaderboards, and a 13-language interface.
Live product · private source |
More product work and architecture notes are available on my portfolio.
Product: TypeScript, JavaScript, React, Next.js, React Native, Node.js, Ruby on Rails
AI & data: OpenAI API, Model Context Protocol, PostgreSQL, Supabase, PocketBase, SQLite
Delivery: Docker, GitHub Actions, Fastlane, Sentry, PostHog
Additional: Python, WebRTC, Home Assistant, Rust/Godot
Most of my day-to-day product development happens in private repositories. GitHub displays that work as anonymized contribution activity without revealing repository names or proprietary code.
For selected private projects I publish safe case studies covering the problem, my role, architecture, engineering decisions, stack, and product walkthroughs. Source code, credentials, customer data, and confidential implementation details stay private.
- Start with the smallest architecture that can survive real use.
- Prefer explicit contracts, tests, and operational documentation.
- Use AI to accelerate engineering while keeping decisions human-controlled.
- Treat security, privacy, and release workflows as product features.
- Build for maintainability after the first successful release.
Based in Chicago · Connected with the Code the Dream community
Connect on LinkedIn for product engineering, AI tooling, and thoughtful technical collaboration.