I build AI systems that ship.

Background

My path to AI engineering wasn't a straight line, and that's where the edge comes from.

I started in graphic design and tattoo work, where precision and intentionality aren't optional. Then I spent two years writing deep technical research on complex systems — protocol architecture, incentive design, market structure — which trained me to read an unfamiliar system fast, find where it breaks, and explain it clearly. When I found LLMs and the Model Context Protocol, I stopped analyzing other people's infrastructure and started building my own.

Mexican, operating globally, fully bilingual — Spanish native, English professional. EST-aligned, async by default.

What I do

I build the unglamorous parts that make AI agents trustworthy in production:

• MCP server design — full primitive set (tools, resources, prompts, elicitations, MCP-UI), SSE + HTTP Streamable transport, multi-tenant architecture.
• Multi-tier agentic orchestration — a lightweight router classifies intent, domain-scoped agents run native tool_use loops with self-correction, and an Expert Registry enforces per-domain tool allowlists with live resource injection.
• Observability & cost control — end-to-end Langfuse tracing on every LLM call, tool invocation, model choice, and token cost, with per-session attribution and failure triage without log digging.
• Human-in-the-loop surfaces — one-click approval flows wired into the agent pipeline for irreversible operations.
• Production discipline — 6,000+ tests, 91%+ coverage, CI/CD, schema validation at every boundary, conventional commits, drift guards. The hygiene that makes solo-built work safe to hand to a team.