Summary
Felipe Uribe is a Senior Machine Learning Engineer with 10 years of experience building production-grade ML systems and data pipelines across industries, currently designing global-scale solutions at Royal Caribbean Group. He combines dual master's degrees in Statistics and Computer Science with hands-on expertise in Python, Spark, AWS, and MLOps tooling to close the gap between notebook prototypes and reliable production deployments. Felipe has shipped models at scale for Mercado Libre and led data and ML engineering efforts at MACH (Banco BCI) and Coca-Cola Embonor, bringing both fintech and large-platform experience. His research background in multi-object tracking—published in Elsevier and Springer journals—and recognition as a Santander NEO Challenge finalist reflect a strong foundation in probabilistic modelling and applied research. Fluent in end-to-end ETL and CI/CD for ML, he is particularly focused on operationalizing experiment tracking and model serving in complex, regulated environments. Based in Santiago, Chile, he pairs academic rigor with a pragmatic, production-first mindset.
10 years of coding experience
5 years of employment as a software developer
Magister en Estadística, Magister en Estadística at Pontificia Universidad Católica de Chile
Magíster en Ciencias de la Computación, Ciencias de la computación, Magíster en Ciencias de la Computación, Ciencias de la computación at Universidad Católica del Maule