I build and deploy high-performance AI systems, primarily focused on quantitative finance.
My work bridges the gap between deep technical implementation and high-level strategic vision. This led me to architect the "Alpha Narratif" market simulator at Amundi and to file my first patent for a stateful AI system.
I operate on two modes:
- Professional R&D: Building end-to-end AI prototypes for quantitative research at Europe's leading asset manager.
- Self-Directed Research: Running a dedicated R&D homelab where I master the full MLOps stack, from bare-metal hardware (
Arch Linux,MLX) to real-time, interruptible conversational pipelines.
Here are the public projects that demonstrate the types of systems I build.
| Project | Category | Key Concept |
|---|---|---|
| patent-low-bandwidth-ai | Patent / Backend | (Patent Pending) A hybrid RAG backend that enables stateful AI conversations over low-bandwidth networks like SMS. |
| gui-agent | AI Agents | A two-layer autonomous agent for macOS, separating visual perception (VLM) from strategic decision-making (LLM). |
| speech-to-speech-pipeline | MLOps / Real-Time | A low-latency (STT-LLM-TTS) conversational pipeline with barge-in, optimized for Apple Silicon (MLX). |
| enigma-shell | Full-Stack AI | An experimental web shell to control a full Linux VM (v86) using natural language via local LLMs (Ollama). |
- AI for Quantitative Finance: Modeling market reflexivity, information warfare, and narrative impact.
- AI Agent Architecture: Designing multi-layer systems (perception, strategy, execution).
- MLOps & Systems: Building high-performance, real-time data pipelines (
asyncio,queues). - Full-Stack Prototyping:
Python(Flask, PyAutoGUI),React(TypeScript),MLX.
You can find the full story of my professional experience on my LinkedIn profile.
