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I design and build advanced AI systems for complex, high-impact environments.

My background spans more than a decade across machine learning, computer vision, signal processing, industrial AI, and large-scale intelligent systems. I’ve led and delivered AI solutions in domains where reliability, scalability, and scientific rigor matter — including defense, aeronautics, industrial operations, and enterprise platforms.

Today, my work focuses on:

  • Large Language Models (LLMs) and agentic systems
  • Retrieval-Augmented Generation (RAG)
  • Multimodal AI architectures
  • AI platform engineering and MLOps
  • Applied reasoning systems and knowledge architectures
  • Production-grade ML systems at scale

I’m particularly interested in bridging the gap between cutting-edge research and deployable engineering: transforming promising AI concepts into robust systems that create measurable operational value.

Open source is an important part of that mission. I use this space to share tools, experiments, architectures, and technical insights that help engineers build more capable, reliable, and maintainable AI systems.

Sponsorship helps support deeper technical work, open-source contributions, long-form engineering content, and ambitious experimental projects.

Featured work

  1. mfournigault/supertonic

    A fork of supertonic to propose simple additional applications

    C++ 1
  2. mfournigault/inverse_compositional_algorithm

    Python implementation of the inverse compositional algorithm

    Jupyter Notebook 1
  3. mfournigault/astro_iqa

    Quality assessment of astronomical images

    Jupyter Notebook 1

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