Software Engineer — Applied AI & ML Infrastructure

I build and deploy AI applications and the software around them.

My work spans model serving, distributed training, retrieval-augmented generation, and backend systems. Previously, I worked on reinforcement-learning infrastructure as an AI Software Engineering Intern at InstaDeep and computer-vision rendering pipelines as a Software Engineering Intern at Prophesee. I studied software engineering at 42 Paris.

A deployed bilingual question-answering service for SEC 10-K and Korean DART filings.

• PostgreSQL/pgvector hybrid retrieval with source-linked answers and source-span citation checks.
• Retrieval evaluation and ablation runs, with token and cost limits for model calls.
• FastAPI streaming backend and a Next.js interface.

A playable AI game with two distinct engines and a distributed training pipeline.

• AlphaZero-style PyTorch self-play and arena evaluation, distributed with Ray and accelerated with C++ MCTS through pybind11.
• A separate C++ Minimax engine with alpha-beta pruning, principal variation search, and transposition tables.
• Nuxt and WebSocket gameplay, deployed on Google Cloud with Docker and Cloudflare Workers routing.

• webserv — C++98 HTTP/1.1 server with non-blocking I/O, poll/kqueue multiplexing, and CGI execution.
• cloud-1 — Linux deployment with Ansible, Docker Compose, Nginx TLS, and persistent database storage.
• ft_services — Multi-service Kubernetes lab with health checks and Grafana-based monitoring.
• kfs — Bootable x86 kernel with interrupt handling, a minimal libc, and a QEMU build workflow.

Based in Paris, France. Open to software engineering, applied AI, ML infrastructure, and backend opportunities in Paris and Île-de-France.

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