Professional Overview
AI/ML Engineer experienced in building and deploying end-to-end computer vision and deep learning systems across agriculture, surveillance, drones, and industrial automation. Skilled in object detection, tracking, classification, pose estimation, video analytics, and production deployment using PyTorch, TensorFlow, OpenCV, Docker, and Hugging Face.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- I led an Omdena Local Chapter in Tunisia, organizing and managing AI Teams
- Built TensorFlow models for olive disease detection, saving farmers 20% of costs
- Built computer vision models for sickle cell diagnosis improving accuracy by 25%
- Led end-to-end ML projects from problem scoping and data creation to deployment
Key Achievements / KPIs
- Achieved 95% accuracy in olive disease detection, saving farmers 20%.
- Improved sickle cell diagnostic accuracy by 25% using computer vision.
- Led ML projects end-to-end, from data creation to deployment.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Automated soccer dataset creation, improving efficiency by 40%.
- Fine-tuned YOLOv7, improving soccer event accuracy by 90%.
- Automated 5-minute highlights, cutting editing time by 80%.
- Deployed a full-stack platform for real-time AI match highlights.
- Translated computer vision research into deployable AI systems.
Key Achievements / KPIs
- Increased soccer dataset creation efficiency by 40% with automated pipelines.
- Improved soccer event detection accuracy by 90% using YOLOv7.
- Reduced highlight editing time by 80% with automated 5-minute reels.
- Deployed a full-stack platform for real-time AI-generated match highlights.
Education
Field of Study: Electrial and Computer Engineering
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.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
