Description

Edge AI and computer vision engineering student with strong interest in building intelligent systems that run efficiently on embedded and edge devices. My work focuses on designing and deploying machine learning solutions that bridge the gap between research models and real-world applications, particularly in perception tasks such as detection, segmentation, and visual understanding for autonomous and IoT systems.

I have hands-on experience with embedded AI environments, including setting up and optimizing deep learning pipelines on constrained hardware platforms. I am comfortable working across the full workflow: data preparation, model training, evaluation, and deployment. I also explore model optimization techniques to improve inference speed and efficiency while maintaining accuracy, which is critical for edge applications.

Beyond model development, I am interested in system-level integration, including sensor data handling, real-time processing, and hardware–software interaction. I enjoy troubleshooting low-level issues in Linux-based environments and building robust AI pipelines for real devices.

I am motivated by practical impact and enjoy working on projects that solve real-world problems in mobility, automation, and smart systems. My goal is to contribute to innovative teams where I can apply computer vision and embedded AI skills to build scalable, production-ready intelligent solutions.

Industry field of expertise

Languages

Arabic
Native or bilingual
English
Fluent
French
Fluent

Workplace preferences

Can work on-site

Tunis (up to 50km)

GENOW
Computer Vision Developer
July 2025 - Today (11 months)
• • Spearheaded the design and deployment of a YOLO-based perception pipeline combining detection and semantic segmentation for the HawkVision ADAS system on NVIDIA Jetson Nano.
• • Drove dataset benchmarking and lightweight model optimization, identifying accuracy-speed trade-offs and accelerating deploy ment with ONNX and TensorRT.
• • Improved real-time robustness on edge hardware through training, data augmentation, and post-processing with Python and fine-tuned Vision-Language Models, contributing 3–6 mAP points of improvement across 15,000+ labeled images and reducing failure cases in low-visibility scenes during internal testing.
• • Initiated an AI-assisted dataset cleaning pipeline and a rule-based vehicle trajectory prediction module to strengthen downstream ADAS decision-making.
• • Keywords: Computer Vision, YOLO, Object Detection, Segmentation, Dataset Engineering, Vision-Language Models, Trajec tory Prediction, ADAS, Embedded AI
Rising Sun Cloud Consulting LTD
IOT project managger
January 2026 - Today (5 months)
The Leader of IoT and AI Projects is responsible for designing, coordinating, and delivering smart systems that combine connected devices and artificial intelligence. This role involves defining technical architecture for IoT ecosystems, integrating sensors, edge devices, and cloud platforms, and ensuring seamless data flow for AI-driven analytics. The leader manages project teams, sets development priorities, and ensures solutions meet performance, scalability, and security requirements. They collaborate with stakeholders to translate business needs into technical solutions, oversee model deployment on edge or cloud, and validate system performance. Strong skills in embedded systems, machine learning, and project management are essential for success.
IoT intelligence artificielle Raspberry Pi Electronique Edge AI