Senior Full-Stack Engineer · AI/LLM Systems in Production
Senior full-stack engineer specialized in taking AI from demo to production.
https://hire-me-mcp-web.vercel.app/api/mcp
Has Marcos worked with event-driven architectures? Show me evidence.
get-skill-evidence({ term: "event-driven architecture" })Yes — "Event-Driven Architecture" is a claimed skill (proficiency: proficient), backed by 1 cited source: Senior Full Stack Engineer, House Numbers. Want the underlying highlight?
Senior full-stack engineer specialized in taking AI from demo to production. Over the past four years I have built LLM pipelines for document data extraction, communication infrastructure that syncs and classifies email and SMS at scale, and agentic workflows that are tested, cost-traced, and measured like any other engineering system. What I care about most is the gap between an AI demo and an AI system: evaluation harnesses, human-graded ground truth, cost discipline, and observability. Full-stack range across event-driven backends, React frontends, and infrastructure.
Core engineer on a 16-service TypeScript monorepo for an AI-powered mortgage-lending platform, working across backend services, frontend, and infrastructure. Joined when the engineering team was three people; the team has since grown to seven engineers, excluding design, PM and QA.
Main backend engineer on a cloud-based, multi-tenant SaaS for real-time quality-control inspections across factories, product lots, and distribution points.
Full-stack development of a platform supporting a fiduciary purchasing agent for FF&E/OS&E procurement, for a US client.
Flagship
Creator and maintainer
This portfolio itself: an open-source MCP server, RAG-backed interview agent and Next.js site over one typed career dataset — built as the demonstrable, inspectable evidence of how I ship production software.
Open-source Python CLI that orchestrates multiple AI coding agents into role-based teams, with phase gates, per-role evals and cost reporting.
Multi-round proof of concept — never deployed — testing whether vision-LLM document splitting, classification and extraction were a viable replacement candidate for the incumbent OCR vendor on a mortgage platform, with human-reviewed corpora, evaluation and cost tracing, and measurement bugs found and fixed along the way.
Solo-built evaluation infrastructure for production LLM features — a labeled corpus, human-graded ground truth, LLM-as-judge scoring, per-call cost tracing and version-controlled prompts.