SB
About
As a results-oriented Machine Learning Engineer with 3 years of experience in computer vision, I've developed a strong track record of designing and deploying robust vision systems for industrial quality control tasks. However, my interests now extend to the rapidly evolving fields of Generative AI and Large Language Models (LLMs), where I can apply my problem-solving skills, data analysis expertise, and passion for innovation to drive impactful projects forward. A strong believer in the power of open-source collaboration, I actively contribute to the machine learning community and am excited about the prospect of joining a team that values innovation, self-learning, and cross-functional collaboration.
Work Experience
2022 - Today
Machine Learning Engineer
Product feature to improve robustness to object position variability
- Analyzed feasibility with image matching PoC.
- Optimization of the deep learning model to comply with timing and embedded hardware requirements : pruned unused layers, maximized GPU usage, minimized GPU ⥦ CPU/RAM data transfers, optimized inference using TensorRT, achieving 12x speedup (from 250ms to 20ms)
- Benchmarking on industrial data and push to production
- With a 99.9% accuracy, the feature delivers near-perfect results, with less than 1 error in 1000 cases.
- Developed a Gradio dashboard for explainability of the final algorithm
MLOps
- Implemented CI/CD pipelines (GitLab CI)
- Leveraged DVC & Ray for efficient data/model management and distributed training.
- Integrated CML for experiment tracking and performance monitoring.
- Automated feature benchmarking on full company dataset.
Led data acquisition and labelling across projects
- Acquired on-site data and launched a company-wide initiative to label 1400 images (text/QR codes). Non-technical staff successfully labeled the data in under 2 weeks, securing the company's competitive edge
- Established a year-long partnership with a third-party labelling company for faster development.
- Oversaw labelling of 33,000 images (150,000 annotations) across various formats (image, video, text, classification).
Package content compliance automation use-case
- Recorded 2.5 hours of on-site video with 3 cameras
- Labeled first 15 minutes using CVAT to infer package count and content (1100 packages)
- Trained classifier to identify packages requiring human action, filtering 46% automatically using image segmentation
Python
MLOps
Lightning
Hydra
Ray
ONNX / TensorRT
Gradio
AWS
Gitlab CI
April 2019 - July 2019
Research Intern
Conducted research on "Discovering Non-Functional Requirements from Published APIs" under Dr. Jordi Cabot supervision
- Designed a web app using a Master-Slave architecture
- Analyzed non-functional properties (latency, uptime) of published APIs across geographically distributedlocations (Europe, America, Asia, ...) using Google Cloud Platform
- Developed a user-friendly dashboard to display analysis results
- Work culminated in a paper published at the ICWE 2020 conference, which received the Best Demo & Paper Award.
Angular
NodeJS
C3.js
Open Source Contributions
Developed and integrated state-of-the-art keypoint detection and image matching models, significantly expanding the library's capabilities in these domains. These models are currently downloaded over 10k times per month.
Education
Katholieke Universiteit Leuven
2019 - 2023
Master's Degree in Computer Science : Major Artificial Intelligence
Thesis : Data-driven optimisation of substitutions in football
Université de Lille
2018 - 2019
Bachelor's Degree in Computer Science