CARLOSRAMÍREZ MARTÍN

Classical history has fascinated me since childhood. How did Rome organize an empire without modern telecommunications? Part of the answer lay in information: harvests, resources and reports that helped people understand the present and anticipate problems. That curiosity about how we make decisions became the thread running through my career.

At Cofidis, I worked in data analysis, demand forecasting and project management. I learned that a good solution starts with understanding the business and needs more than technology: clear goals and teams that understand each other. My Scrum training reinforces that approach.

That journey led me to deepen my knowledge of data science, Python and machine learning through studies at UOC and Tokio School. Today, at MELMACIA LAB, I work on data preparation and analysis, pipeline automation, and model training and validation.

I’m now extending that perspective to AI engineering and building applications based on foundation models. My interests include prompt engineering, RAG systems and connecting data, models and interfaces to turn their capabilities into useful tools that can be evaluated.

That is where I position myself as a Full-Stack AI Engineer: at the intersection of data, business and building solutions. The tools change, but the question remains: how can we turn what we know into better decisions?