Building scalable intelligence and leading technical teams to transform complex data into purpose-driven systems.
"I believe in the intersection of mathematical rigor and elegant systems. My work focuses on removing friction from the ML lifecycle—moving from experimental code to reliable, automated production environments."
Managed cross-functional teams of up to 14 people, balancing technical oversight with strategic planning.
Built end-to-end platforms with Airflow and Docker, reducing operational bottlenecks significantly.
Bringing academic rigor from UNAM to solve real-world financial and operational challenges.
A collection of privacy-focused, browser-based image tools that blend traditional image processing with machine learning techniques. It operates without tracking and keeps your data local to your device, ensuring your files remain private.
A full-stack data science project that demonstrates modern data engineering, machine learning, and cloud deployment practices. This platform simulates a real-world e-commerce ecosystem, transforming raw data into actionable business intelligence through a complete ML pipeline.
A modular, production-ready MLOps system for retail demand forecasting. This project demonstrates how to build, validate, and deploy time series forecasting models using traditional statistical methods and machine learning, with a focus on reproducibility and scalability.
I am always open to discussing ML projects, technical leadership, or innovative system design.