Professional Overview
AI Engineer with a Computer Vision and Deep Learning background, focused on building end-to-end ML pipelines that go from prototype to measurable production performance. Experienced in PyTorch-based model development, model evaluation and failure analysis, and industrializing AI systems under real-world latency and data-quality constraints
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Engineered a multimodal audio deepfake detection system
- Guaranteed the methodological quality of the AI solution
- Defined the foundational data strategy for acoustic analysis
- Industrialized the deepfake detector for unlimited-duration audio
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Developed a multilingual (Arabic/French) sentiment classi er
- Industrialized the sentiment analysis pipeline for continuous, automated process
- Generated relevant insights for business stakeholders via interactive Dash
Education
Field of Study: Data Science & Analytics
Field of Study: Big Data
Field of Study: Information Systems Development
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
