Description

AI Engineer — I help businesses turn their data into concrete, deployed solutions: machine learning models, data pipelines, generative-AI applications (RAG, LLMs), and custom automations.

From design to production, I handle projects end to end: data exploration and cleaning, predictive modeling, deployment (API, Docker), and integrating AI models into your business tools — with clean, well-documented code.

What sets me apart: a results-driven approach. My projects don’t stay in a notebook — I deploy them and make them work in the real world (GitHub portfolio + a live personal project, Persei).

Trained in data science and big data (master’s level), I work in French, Arabic, and English.

Need a predictive model, a dashboard, a chatbot/RAG system, or an automation? Tell me about your project and I’ll propose a clear, tailored solution.

Languages

French
Native or bilingual
English
Conversational
Arabic
Fluent

Workplace preferences

Can work on-site

Annecy (up to 50km), Annemasse (up to 30km)

Lynn Meraki LTD
DATA Engineer Freelance
November 2023 - September 2025 (1 year and 10 months)
Engineered and implemented a comprehensive AIS processing pipeline at Lynn Meraki, encompassing data ingestion, cleansing, feature engineering, and navigation flow visualisation. Formulated predictive models to estimate destinations and Estimated Time of Arrival (ETA), followed by the deployment of an automated production version. Executed a strategic sampling strategy and historical trajectory comparison to enhance the robustness of predictive models. Employed an array of tools including Python, TensorFlow, Git, Pandas, NumPy, Scikit-Learn, and Jupyter Notebook for various stages of data processing and model development.
Python Machine learning
Paragraph Studio
DATA ENGINEER
April 2015 - September 2023 (8 years and 5 months)
Directed the Data Engineering Team at Paragraph Studio, pioneering the development of an innovative smart mirror for virtual fitting room experiences. Oversaw the successful integration of AWS technologies to deploy machine learning models, significantly enhancing system performance and reliability. Supervised the design and implementation of a parallel processing pipeline, facilitating simultaneous handling of multiple user interactions. Utilised an array of tools including AWS services, OpenPose, DensePose, and Computer Vision techniques for advanced project development. Applied Semantic Segmentation algorithms to augment the accuracy and efficiency of the virtual fitting room feature. Programmed in Python and C++ to develop robust, scalable code for real time data processing and model deployment. Leveraged CUDA and PyTorch for high-performance computing tasks, optimising the system for speed and efficiency. Maintained version control and collaborative development using Git, ensuring a smooth workflow and team coordination.
Python Machine learning LLM RAG