I'm an AI Engineer with experience building multi-agent systems, RAG pipelines, on-premise systems and LLMOps/MLOps platforms
I'm an AI Engineer specialized in designing and building artificial intelligence systems. My focus is on solving complex business problems through secure data pipelines, automated Machine Learning systems, and scalable Multi-Agent Architectures with advanced RAG systems.
My journey started in Full Stack Web Development, evolved into DevOps and Data Science, and now I specialize in Agent AI Ops, LLMOps, and MLOps. I believe the biggest challenge in AI today isn't just creating new models, but bringing them to production securely and profitably.
I focus on designing automated pipelines, establishing guardrails and sandboxes, and prioritizing data sovereignty and GPU infrastructure execution to build reliable and scalable systems for real-world environments.
"From code to data, and from data to AI"
Production-grade systems, not proofs of concept.
Server-Driven UI platform that translates natural language into fully interactive React components in real time. A 5-node LangGraph pipeline with dynamic MCP tool discovery, hybrid inference (Gemma 2b local + Gemini Flash cloud), and GraphRAG over Neo4j + Qdrant + PostgreSQL. End-to-end latency under 2.5s. Zero hallucinated UI via Pydantic contracts.
End-to-end patient journey orchestration platform — a 24-container intelligence layer deployed over existing legacy systems without disruption. Multi-agent LangGraph pipeline, event bus on Redis Streams, ML anomaly detection (Isolation Forest + XGBoost), and production observability from day one.
LLMOps automation system driven by Genetic Algorithms — autonomously evaluates, mutates, and optimizes LLM inference configurations without human intervention. Reproducible pipelines via Apache Airflow + LangGraph. Transforms manual parameter tuning into a production workflow.
End-to-end supply chain intelligence pipeline on Azure — predictive resampling models coupled to linear programming algorithms, projecting 15–30% reduction in overproduction. Automated BI via Power Automate + live Power BI dashboards. Team lead.
Local-first research and operations platform for multi-agent systems. Designed for practitioners who need complete observability and control — not a cloud sandbox. Covers agent design, integration pipelines, GPU inference, and structured evaluation workflows.
20-container self-hosted AI lab running on RTX GPU-accelerated local inference with Ollama, vector search (Qdrant), ComfyUI, and full LLMOps observability (Prometheus + Grafana + cAdvisor + NVIDIA Exporter). Built under strict IaC and DevOps principles. Zero cloud dependency.
Containerized IT support platform built for UASLP — real-time ticketing system with RBAC, Laravel + React/TypeScript frontend. Deployed with Docker + Kubernetes, CI/CD pipelines, and real-time monitoring. 99.9% uptime. 40% reduction in incident detection time. 25% faster request resolution.
Architected a dual-component enterprise AI system based on Clean Architecture: a self-hosted Multi-Agent System for CRM operations and a Local System for confidential institutional archive management. Built a 100% on-premise RAG pipeline using Qdrant and NVIDIA GPU-accelerated local inference (Ollama), guaranteeing absolute data sovereignty.
Tools I reach for in production — not a skill checklist.
Open to full-time, contract, or consulting engagements — remote or on-site, LATAM or international.