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. ”

Gilles Legoux
Senior Software Engineer
GitHub LinkedIn Stack Overflow Medium

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

Follow me on these different platforms, my pseudo is either avatar glegoux and/or gilleslegoux: