Antonino Ingargiola
AI And Big Data Engineering Lead
Milan, Lombardy, Italy
Summary
👤
Senior🎓
Top SchoolAntonino Ingargiola is an AI and Big Data engineering lead with 12 years of experience building production-grade data platforms, ML solutions, and engineering organizations. Currently at Agile Lab he helps enterprises adopt Data Mesh, shape data strategy and deliver ML-driven platforms, drawing on prior startup experience as co-founder and CTO of smartFAB where he commercialized AI for manufacturing. His background spans academic research—developing open-source tools and hardware-software systems for single-molecule spectroscopy at UCLA—and industrial productization, giving him a rare fluency across research, engineering and product. A hands-on contributor to scientific Python projects (notably improving lmfit and Jupyter’s nbconvert execute flow), he blends pragmatic software craftsmanship with strong statistical and systems thinking. He is especially attentive to Conway’s Law in practice, aligning team structure and governance to achieve resilient architectures.
12 years of coding experience
11 years of employment as a software developer
Ph. D., Single-Photon Avalanche Diodes modeling, simulation and development, A, Ph. D., Single-Photon Avalanche Diodes modeling, simulation and development, A at Politecnico di Milano
Italian, English
Github Skills (12)
model-building10
error-handling10
scipy10
python10
nbconvert10
jupyter10
modeling10
documentation10
model-driven-development10
model-driven10
numpy9
traits9
Programming languages (11)
Github contributions (5)
lmfit/lmfit-py
Aug 2014 - Aug 2016
Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize, and with many additional classes and methods for curve fitting.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:58 commits, 21 PRs, 106 comments in 2 years
Contributions summary:Antonino primarily focused on enhancing the `lmfit-py` library by adding new features and refining existing ones. They introduced a "name" attribute to the Model class and implemented its usage in the documentation. The user also addressed typos and improved the handling of composite models by implementing features such as the propagation of hints and parameter merging. Furthermore, the user enhanced the `Parameters` class and the `Model` class overall by implementing additional methods such as pretty printing and by fixing issues related to the calculations of statistics such as AIC and BIC.
linear-least-squaresminimizationpythonoptimizationadditional
jupyter/nbconvert
Sep 2015 - Jan 2016
Jupyter Notebook Conversion
Role in this project:Back-end Developer & Technical Writer
Back-end Developer & Technical WriterContributions:27 commits, 3 PRs, 33 comments in 3 months
Contributions summary:Antonino primarily contributed to the `nbconvert` project by modifying the `ExecutePreprocessor`. Their work focused on improving error handling within this component, ensuring that execution errors are properly captured and displayed, and enhancing the user experience through better error messages. Additionally, they refactored the code for clarity and added documentation for the `ExecutePreprocessor` and its methods, including examples and command-line usage. This indicates a focus on improving the reliability and usability of the notebook execution feature.
jupyter-notebookconversionjupyternotebook
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.