Software engineering student at École 42 (RNCP Level 7, Master's-equivalent), specializing in AI & Algorithmics.
I build high-performance, low-level systems in C++ — and apply them to markets: real-time order-book reconstruction, backtesting engines, and automated execution. I care about what happens under the abstraction: memory layout, allocations, and latency.
Currently looking for a 6-month internship in C++ systems / quantitative development.
Languages
C • C++ • Python • Bash
Systems & Performance
I/O Multiplexing • Concurrency • RAII • Manual Memory Management • Eigen • Valgrind • gprof
Markets
Order Book Reconstruction • Backtesting • Real-time Market Data • Algorithmic Strategies
Tooling
Git • Linux/Unix • Make • CMake • Docker • GitHub Actions • AWS
Real-time market-data pipeline: incremental local order-book reconstruction over the Binance WebSocket, with consistency checks and desync detection. Deployed on AWS EC2 (Singapore) for minimum latency.
Python asyncio WebSocket Order Book AWS
Feed-forward neural network from scratch in C++/Eigen — hand-written forward/backward propagation, Strategy-pattern activations & optimizers, zero-copy batching via Eigen::Ref.
C++ Eigen Machine Learning Performance
HTTP/1.1 server from scratch in C++98: non-blocking I/O multiplexing via poll(), CGI, chunked encoding — stress-tested to 1,000 concurrent requests.
C++ poll() HTTP/1.1 CGI Concurrency
POSIX-compliant shell in C: lexer → parser → executor, with process and memory management.
C Unix Parsing Process Management
Also: Cub3D (raycasting 3D engine in C) · Inception (Docker infrastructure & monitoring)
- 📄 Minishell 42 : l'art de créer un shell performant et maintenable
- 📄 Écrire du code bug-free : leçons de la programmation à haut risque
- 📄 Semantic versioning on autopilot : vos commits pilotent votre pipeline CI/CD
