Daniel Egbo’s career bridges two worlds that have more in common than they might seem. As an astrophysicist and PhD candidate at the University of Cape Town, he works with radio astronomy, transforming raw observations from instruments like MEERKAT into data that enables multi-wavelength science, including the delicate task of cross-matching sources across optical and radio catalogs. Astronomy taught him how to build pipelines that stay reliable when the data is messy and the science is unforgiving.

He has since brought that rigor into applied machine learning as a machine learning engineer, building end-to-end ML and LLM applications with a focus on reliability. His work spans knowledge-retrieval assistants, practical evaluation of language model systems, and the engineering practices needed to move models from research demos to production. He also applies data science back to astronomy, keeping one foot in each field.

Daniel serves as an AI ambassador for Arize and Tavily and is a strong believer in learning in public, sharing what he builds and evaluates so that others working on LLM applications and scientific ML can learn from both his successes and his mistakes.