Rafael Cartenet
ML EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Lead Staff Data Engineer at Blitz
As a problem-solving enthusiast, I’ve always been driven by curiosity and a desire to tackle technical challenges that teach me something new. My passion for data started early, and I’ve since explored it in many forms — from sequential and spatial data to images, audio, and BI systems. Now working in the exciting world of gaming, I build large-scale data systems that power analytics, in-game features, and machine learning for millions of players. With a strong R&D mindset, I enjoy combining deep technical work with creative thinking — whether it’s optimizing pipelines, experimenting with models, or enabling better decisions through data. I thrive in environments where I can learn continuously and push the boundaries of what data can do.
Korea Advanced Institute of Science and Technology
Master of Engineering - MEng, Artificial Intelligence
January 1, 2016 – January 1, 2017
INSA Rouen Normandie
Master of Engineering - MEng, Mathematics and Computer Science
January 1, 2012 – January 1, 2016
Blitz
Lead Staff Data Engineer
November 1, 2025 – Present
Remote
Blitz
Lead Staff Data Engineer
June 1, 2022 – November 1, 2025
Remote
xCO Analytics
Lead Data Engineer
December 1, 2021 – June 1, 2022
xCO Analytics
Lead Data Engineer
February 1, 2021 – December 1, 2021
Ample Meter
Machine Learning Engineer
July 1, 2020 – September 1, 2020
Shanghai, China · Remote
Openfield Live
Machine Learning Engineer
June 1, 2018 – February 1, 2021
Paris Area, France · On-site
Mediasia Labs
Machine Learning R&D Engineer Intern
September 1, 2017 – February 1, 2018
Shanghai City, China
KAIST
Research Assistant - AIM Lab (Artificial Intelligence & Machine-learning)
March 1, 2017 – June 1, 2017
Daejeon, South-Korea · On-site
AEROW
Software Development Engineer Intern
May 1, 2016 – August 1, 2016
Paris, France · On-site
BASSETTI
Integration Developer Intern
July 1, 2015 – September 1, 2015
Shanghai, China · On-site
Mini AlphaGo Implementation
January 1, 2017 – Present
Implementation of a simple version of AlphaGo (Go game artificial intelligence) using two different Deep Neural Nets and game theory plus Monte Carlo Tree Search. - Data extraction and processing - Design Models - Adversarial Reinforcement learning based Learning Technologies : - Python - Tensorflow
Data Driven OCR Implementation
August 1, 2016 – Present
Designed and implemented data driven Optical Character Recognition tool using machine learning techniques for Bank cheque CMC7 code extraction. - Data Extraction and preprocessing - Training with data - Prediction Technologies : - Python - Scikit Learn (SVM) - PIL (Image processing)
Fast Fourier Transform algorithm implementation
January 1, 2013 – Present
Implemented Fast Fourier Transform Cooley-Tukey algorithm using Pascal. - Understanding general Fourier Transform - Understanding and implementation of the fast algorithm Technologies : - Maple - Pascal
Microsoft Certified: Azure Data Engineer Associate
Microsoft
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from personal AI implementations to large-scale data engineering at companies like Blitz and xCO Analytics, indicates adaptability and a broad interest in the ML/Data domain. Their progression through various roles and companies, including a research assistant position at KAIST, suggests a proactive and continuous learning mindset. The target role of ML Engineer aligns well with their academic background and professional experience, particularly their early career focus on ML engineering and later integration of ML into data pipelines.
Soft Skills & Operational Fit
The candidate's experience as a Lead Staff Data Engineer and Scrum Master suggests strong leadership, communication, and agile methodology adherence. Their role in 'bridging analytics, engineering, and ML' and leading internal workshops indicates good cross-functional collaboration and mentorship abilities. The project descriptions are clear and highlight problem-solving and implementation skills.