Pastor Soto is a Machine Learning Engineer and mentor with a practical bias toward shipping. While studying medicine at the university, he took on side work that pulled him into machine learning, and his career progressed through roles as a statistician, data analyst, and data engineer, with skills growing from SPSS and R into Python. The inflection point came with ML Zoomcamp: by publishing his exercises and projects publicly, he attracted interviews and job offers, and early freelance gigs on Upwork taught him the trade by doing.

Today Pastor’s work centers on production ML: moving data, wiring APIs, containerizing models with Docker, deploying to AWS, and feeding LLMs so applications actually run. His medical studies background gives him a specialty in healthcare machine learning, from building portfolios on clinical datasets to shipping models that frontline teams can rely on.

Pastor mentors with DeepLearning.AI and leads sections for Stanford’s Code in Place, paying forward the guidance that accelerated his own transition. He writes about focused learning, public portfolios, and the small, finished steps that compound into a career, encouraging career changers to learn by building in the open.