Thomas Rohée
Développeur IA at Vosker
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolThomas Rohée is an AI developer in Montreal with 12 years of experience building production ML pipelines, SaaS applications, and real-time optimization systems. He has delivered healthcare-focused models—predicting patient readmission risk and order stockouts—while also crafting shared cloud-native services for multi-tenant SaaS and single sign-on. Comfortable across Python, Scala, Node.js and Java, he deploys projects with Databricks, AKS/EKS and CI/CD tools like Jenkins and Azure DevOps. His background includes hands-on RTB optimization work where he tuned online ML systems for campaign ROI and contributed to the pylearn2 library to improve model monitoring visualizations. He’s experienced in coordinating academia-industry grants and mentoring ML interns, blending research collaboration with product-driven delivery. This mix of applied research, operational ML, and cloud engineering makes him effective at turning complex models into reliable, production-ready features.
12 years of coding experience
9 years of employment as a software developer
Master of Science, Computer Science, Master of Science, Computer Science at Université de MontréalBachelor of Science with Honours, Computer Science and Information System, Bachelor of Science with Honours, Computer Science and Information System at SUPINFO - The International Institute of Information Technology
French, English
Github Skills (14)
machine-learning10
python10
theano10
probabilistic-reasoning9
probabilistic-models9
statistical-models9
probabilistic-programming9
data-modeling9
algorithms8
numerical-optimization8
code-optimization8
algorithm8
optimization8
optimisation8
Programming languages (2)
Github contributions (5)
lisa-lab/pylearn2
Feb 2014 - Feb 2015
Warning: This project does not have any current developer. See bellow.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:87 commits, 5 comments in 1 year
Contributions summary:Thomas contributed to the `pylearn2` repository by modifying and implementing features related to machine learning model monitoring and probabilistic maximum pooling. Their work involved adjusting the plot width for legends in the `plot_monitor.py` script, improving the visualization of model performance. The user also merged updates from the central master branch and made code modifications within the `pylearn2/expr/probabilistic_max_pooling.py` file.
javascripttypescript
Menerve/menerve.github.io
Feb 2015 - Apr 2023
Contributions:28 pushes in 8 years 4 months
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.