Experience
Product Engineer, Applied AI
Anthropic
September 2025 - Present
London, UK
- Advise clients to design and deploy Claude-powered solutions
- Provide clients with deeply technical guidance on AI system architecture, optimization, and best practices
- Collaborate with product and research teams to inform feature development with field insights
AI & Data Senior Consultant
EY
September 2022 - August 2025
Brussels, Belgium
- Designed and deployed GenAi applications including code generation tools, RAG systems, and process augmentation solutions for enterprise clients
- Developed predictive ML models for financial services using gradient boosting and ensemble methods (XGBoost, CatBoost, scikit-learn...)
- Contributed to academic research on AI safety, publishing two conference papers on adversarial attacks
- Led GTM strategies, facilitated stakeholder management, and contributed to client development
Education
Master of Computer Science
KU Leuven
2020 - 2022
Leuven, Belgium
Major in Artificial Intelligence. Master Thesis: Usage of Gaussian Mixture Models for the classification of (sub)genres of (Electronic Dance) Music
Bachelor of Civil Engineering
KU Leuven
2016 - 2020
Leuven, Belgium
Major in Computer Science. Bachelor Thesis: Casting to multi-screen videowall
Academic Publications
Belmoukadam, O., De Jonghe, J., Sassine, N.
2nd ICML Workshop on New Frontiers in Adversarial Machine Learning (2023)
Belmoukadam, O., De Jonghe, J., Ajridi, S.
NLAICSE 2024
Personal Projects
LLMFromScratch
Recreating an LLM completely from scratch, written in C. The goal is to be a learning experience for people of all varieties of technical expertise. We start all the way back from the perceptron and gradually build up to an LLM. Alongside the code, I provide a clear explanations on two levels: one for less technical people who just want to understand how it works and to develop an intuition, and one for people that want to dive into the technical details and are familiar with basics of mathematics.
InstaNER
A CLI tool that automates the entire process of creating a Named Entity Recognition (NER) model, starting all the way from identifying the needed entities, creating train and test data, training a transformer based model on the data, evaluating the results and loading it for inference, all while ensuring reproducability. The goal is to provide an easy-to-use accelerator allowing non-technical people to start training their own NER models, showing the process of synthetic data generation and how this can speed up the model generation process.
Skills
Programming Languages
Python
Proficient
Python has been my bread and butter to work as data scientist and AI engineer. I've used it extensively in both industry and academia.
C
Proficient
To understand anything low-level, C has been my go-to language for development. I've used this extensively for personal projects.
Go
Intermediate
Currently upskilling myself in Go with the goal to build future-proof systems. I believe this is an amazing language for building robust AI systems. Primarily used for personal projects, but I've used it in industry.
HTML / CSS / JavaScript / TypeScript / ReactJS / htmx
Intermediate
Learned a decent amount of web-tech to build full-stack applications. I've used this occasionally in industry.
Frameworks and Tools
Tooling
Jupyter Notebooks, Neovim, VSCode, Git, GitHub, Docker, Nginx, tmux, LaTeX, (Arch) Linux
Frameworks
NumPy, Pandas, PyTorch, TensorFlow, Keras, pytest