Alexandre Brillant
Software Engineer at EquiSoft
Quebec, Canada
Summary
🤩
RockstarAlexandre Brillant is a software engineer based in Quebec, Canada with a decade of hands-on experience building both front-end mobile interfaces and machine learning pipelines. He contributes to open source, notably refactoring a seq2seq RNN signal-forecasting project to use the Neuraxle pipeline framework and improving model architecture, data loading, and visualization. On the front end he has implemented polished React Native UI features—animations, card margins, and swipe-direction controls—for a Tinder-like deck swiper. Alexandre combines practical ML engineering with user-facing product work, able to move models from prototyping toward structured pipelines while refining UX. His profile suggests a pragmatic developer who enjoys bridging data science and app UX, with a particular knack for reorganizing codebases for maintainability and clarity.
10 years of coding experience
Stackoverflow
3,538reputation
336kreached
70answers
51questions
ionic-framework
top-1%
angular
top-5%
Github Skills (25)
javascript10
react-native10
data-pipelines10
python10
animation10
data-science10
machine-learning10
rnn-model10
ionic10
ui-design10
n10
seq2seq10
uid10
ionic-framework10
tensorflow10
Programming languages (9)
Github contributions (5)
alexbrillant/react-native-deck-swiper
Apr 2017 - Mar 2019
tinder like react-native deck swiper
Role in this project:
Front-end Developer
Front-end DeveloperContributions:176 commits, 61 PRs, 162 pushes in 1 year 11 months
Contributions summary:Alexandre contributed to the UI of the React Native deck swiper, implementing and refining styles. They added new components and features, such as opacity animations and margin adjustments to the cards. The user also integrated new features for enhanced usability, like the implementation of swipe direction functionalities and improvements for the swiper's overall structure.
tinder-likereactdeckreact-nativereact-native-component
guillaume-chevalier/seq2seq-signal-prediction
Jan 2020 - Jan 2020
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
Role in this project:ML Engineer
ML EngineerContributions:56 commits, 5 PRs, 9 comments in 10 days
Contributions summary:Alexandre refactored and updated the code to use Neuraxle, a deep learning pipeline framework. The commits show a transition from a previous implementation to a more structured approach utilizing Neuraxle. The user was focused on defining the model architecture, loss functions, optimizers, and data loading for a sequence-to-sequence (seq2seq) recurrent neural network (RNN) model, demonstrating expertise in building and integrating machine learning models within a data science project. The user improved on the use of deep learning pipelines, the implementation of the model, and added model plotting functionality.
forecastingsequencernnpythonsignal
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