Hello :wave:, I am a senior software engineer at criteo-ai-lab Criteo AI Lab.
I share :newspaper: tech blog posts about my works and interests, mostly about data Data + ai AI. ”

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Gilles Legoux
Senior Software Engineer
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Software Engineering


About me

Currently, I am based in eiffel-tower Paris. I work as senior software engineer in the criteo-ai-lab Criteo AI Lab, inaugurated in 2018, in the first 🦄 french unicorn: Criteo. This company contributes, since its creation in 2005, machine learning ecosystem, in the sector of the ad tech, with an mindset to defend a open-internet free and open internet; mixing data data engineering and artificial-intelligence artificial intelligence challenges at high-scale high scale for research-development research and development. More particularly, I am responsible since 2020 to build an enhanced and unified e-commerce catalog composed of +30K catalogs and +25B products made through import and enrichment steps with data pipelines offline and online executing AI processors about categorization, "brandization", and de-deduplication of e-commerce products especially. This dataset huge e-commerce dataset is consolidated with transactional and behavioral consumer data, then used by ai-engineonline engines to recommend and search e-commerce products to which I also contribute, it evolved and is evolving gradually to deep-learning deep learning and llm large language models. These tools are available for the worldwide e-commerce market on the 3 main business areas: AMERICA, EMEA and ASIA, and are accessible via a SaaS platform for demand and supply chains.

I explored all the different tech aspects from hardware to software through ecosystems in startup start-up and multinational-company multinational organization for 10+ years. I am very excited by the start-up company world from the process of creation to the evolution of a large company. Before I worked as software reliability engineer and web full stack developer. After advanced studies in engineering and research and these accumulated professional experiences, I acquired soft and hard skills in computer-science computer science, data-science data science, and applied-mathematics applied mathematics with a specialization in information systems, machine learning, and big data.

Here my stack technical stack, that is to say, some technologies that I have already used. It is non-exhaustive, new technologies occur and disappear over time, and the list continues to increase, and I use only some of them in the function of the project on which I work:

I love coding algorithms or/and doing applied mathematics. I am passionate about computer science and data science, and more generally about informatics and mathematics. My favorite programming languages, among those I use, are:

java Java | scala Scala | python Python | bash Bash.

under the GNU/linux GNU/Linux operating system with the distribution ubuntu Ubuntu for the development

One of my preferred formulae is the Euler's identity:

$ e^{i\pi} + 1 = 0 $ .

Else, I like :rugby_football: rugby, :soccer: soccer, mtga magic the gathering arena, and the juggling, especially the diabolo “diabolo”.

Since the beginning of my educational and professional career, I followed this mindset:

Venn diagram to master computer science, data science and applied mathematics
Venn diagram to master computer science, data science and applied mathematics [built by author]

I started my education in applied-mathematics applied mathematics and statistics, developed 🏴‍☠️ hacking skills, then explored the different 📊 domain expertises. Now, I am mixing and practicing all these skills. These competencies allow browsing in each job position and role: software (hardware) engineer, machine learning engineer, data scientist, data analyst, business intelligence engineer, data engineer, entrepreneur and researcher required to develop a project of data science, where the :clipboard: researcher and :computer: software engineer plays an essential role: the first one is in advance to the industry providing the potential applied and fundamental theory for the :rocket: future and the second one is a 🧬 generic position to be transverse, existing before these specializations. While data analyst, entrepreneur and business intelligence engineer are more linked :dollar: financial aspects, and are among the business stakeholders.

AI processor with machine learning lifecycle
AI processor with machine learning lifecycle [built by author]

Contact me

Contact me, and I will answer you in avatar under 24h and often more quickly again:

If you are a peer, a reader, or a editor, give me feedback or suggestions about the content that you would like to have. So I can publish content on topics or media that you are interested in.


If you are a start upper, explain me your project.


If you are a recruiter, Please get in touch with me via LinkedIn, and I will answer you. If I am not available for a new job opportunity, I could redirect you to another person in my network. But please, take into consideration my profile by being relevant.


If you are an events organizer, I am often interested in being a guest speaker.


If you are a researcher, I am curious to exchange about your research challenges, to be interviewed, or to review papers.


If you are a student or a teacher, I give courses as a teacher or teaching fellow about computer and data science for informatics and mathematics.


Else it will be pleasure to discover who you are!


Follow me

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