Ricardo García Ramírez

Hey there! I'm a full-stack developer and data scientist. I build AI/ML systems that run in production on the cloud, across machine learning, data engineering, cloud infrastructure, and full-stack delivery.

I'm currently a Senior Developer at MSCI, building backend services, analytics tooling, and data workflows for large-scale financial data systems. I hold an M.Sc. in Data Science from Pontificia Universidad Católica de Chile, with a thesis on probabilistic reconstruction of the Purkinje network from an electrocardiogram using computational modeling and Bayesian inference.

My depth sits where data engineering, machine learning, and practical software delivery meet. I've built reproducible ML pipelines in JAX and scikit-learn for real research problems, ETL workflows that take raw files all the way to queryable analytics assets, and RAG systems from the vector-chunking layer through retrieval and LLM orchestration.

My background is in biomedical engineering, where the gap between "works in theory" and "works on a patient" has real consequences. That's why my thesis work was not a detour from that engineering mindset: it is where I sharpened my approach to uncertainty, reproducibility, and proving a system is correct rather than assuming it.

My freelance work focuses on Python tools with clear scope and a real handoff: automation pipelines, data-extraction workflows, RAG/LLM applications, dashboards, and scientific Python packages.

Underneath my software development work is a research and teaching track: peer-reviewed publications in biosensing and BioMEMS, teaching bioinstrumentation courses at Tec de Monterrey, and regularly writing on software engineering best practices and Data Science workflows. That background is why I default to documented, tested, reproducible code over notebooks.