Thomas Epelbaum
Head Of Python Developments And ML at EcoAct France
Greater Paris Metropolitan Region France
Summary
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Rockstar🎓
Top SchoolThomas Epelbaum is a Head of Python Developments and ML with 10 years of experience building production-grade ML systems and backend platforms, currently leading Python and ML efforts at Schneider Electric Sustainability via EcoAct France. He manages and mentors a team of senior engineers (including PhDs), operating and securing an ecosystem of 30+ applications and 50 services while shipping bespoke FastAPI/SQLModel applications and CI-driven Docker stacks. Thomas has a strong research pedigree (PhD-level physics), production ML research background from Shift Technology, and a history of creating open-source tooling—most notably the ecodev-doc project to accelerate SMEs’ development. He combines deep technical craftsmanship (high test coverage, ML pipelines, MLOps) with product-driven delivery and a knack for turning research ideas into operational services.
10 years of coding experience
8 years of employment as a software developer
Master, 2nd year : Fundamental concepts of physics, Theoretical and Mathematical Physics, 16.7/20, 4th over 29, Master, 2nd year : Fundamental concepts of physics, Theoretical and Mathematical Physics, 16.7/20, 4th over 29 at Ecole normale supérieureEngineer, Master's degree, Modeling, Virtual Environments and Simulation, Engineer, Master's degree, Modeling, Virtual Environments and Simulation at ENSTA ParisTech - École Nationale Supérieure de Techniques AvancéesTheoretical and Mathematical Physics, Theoretical and Mathematical Physics at University of TorontoEcole polytechnique
French, English
Github Skills (15)
keras10
feedforward10
recurrent10
architectures9
deep-learning8
machine-learning8
tensorflow8
neural-network7
test-data3
parameterized3
testing3
python2
unit-testing2
test-automation1
test-framework1
Programming languages (2)
Github contributions (5)
tomepel/Technical_Book_DL
Sep 2017 - Oct 2019
This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent.
Contributions:25 commits, 6 PRs, 34 pushes in 2 years 1 month
architecturesdeep-learningconvolutionalmachine-learningneural-network-architectures
tomepel/Stat_inference
Dec 2015 - Dec 2015
Contributions:6 pushes, 1 branch in 2 days
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