I build the backend infrastructure that sits between AI and production systems — the middleware, data gateways, and developer tooling that make AI integrations safe, observable, and maintainable.
Most teams hit a wall after the demo stage: the AI works, but connecting it to real databases, live event streams, and internal knowledge bases without creating security or operational problems is a different kind of engineering. That's what I focus on.
Secure AI-to-database access layers. Webhook classification and event-driven automation. Semantic search and retrieval infrastructure. MCP servers and AI tooling backends. All built with clean architecture, thorough test coverage, and production-minded defaults.
TypeScript · Node.js · .NET · PostgreSQL · MCP · Vector databases · AI integrations · Backend systems · Docker
Backend architecture, AI infrastructure, and integration-heavy projects. See pinned repositories below.