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Jawhar Marzougui

@jawharmarzougui

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Telecommunications engineer specializing in AI-driven computer vision solutions.

Tunisia

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What I'm looking for

I seek a technical role applying computer vision and ML to real-world problems, in collaborative teams that value impact, learning, and deployment of scalable AI solutions.

I am a graduate engineer in telecommunications with a specialization in multimodal information applications, focused on artificial intelligence, intelligent systems, and technological innovation. I have delivered research and applied projects—developing CNN-based classifiers, 3D-CNN temporal models, and real-time dashboards—achieving notable results such as 93.4% accuracy on a proprietary SD-WAN image dataset and 0.89 AUC on targeted emotion detection.

Comfortable across the full ML lifecycle, I work with Python, PyTorch, TensorFlow, OpenCV and deployment tools like Docker and Streamlit, and I bring curiosity, autonomy, and meticulous technical communication to multidisciplinary teams where I can drive high-impact solutions.

Experience

Work history, roles, and key accomplishments

Current

Customer Support Specialist

Current

GoMyCode

Feb 2025 - Present (8 months)

Provide daily learner and client support by resolving inquiries, assisting with platform use, and tracking tickets; contributed to educational content creation and coordinated voucher distribution with sales and educational teams.

Customer Support Content Creation Ticketing Educational Technology Communication

AI Engineer Intern

LEAT (University Côte d'Azur)

Apr 2024 - Aug 2024 (4 months)

Developed an intelligent system for network congestion detection using dynamic topological visualizations and CNNs, training PyTorch models to 93.4% accuracy on a 25,000-image proprietary dataset and delivering a Streamlit dashboard for real-time alerts.

PyTorch Convolutional Neural Networks Computer Vision Streamlit Model Training Python

Computer Vision Intern

Medicus Clinic

Jun 2023 - Aug 2023 (2 months)

Designed a facial micro-expression detection pipeline using OpenCV and Mediapipe on 15,000+ frames and trained a 3D-CNN in TensorFlow achieving 0.89 AUC for targeted emotions, integrating predictions into a medical interface for clinicians.

OpenCV Mediapipe TensorFlow Feature Extraction Model Evaluation

Education

Degrees, certifications, and relevant coursework

Higher School of Communications of Tunis (Sup'Com)

National Engineering Degree, Information and Communication Technologies

2021 - 2024

Activities and societies: Academic and personal projects in programming, data analysis, modeling; worked with Python, PyTorch, TensorFlow, OpenCV.

National Engineering Degree in Information and Communication Technologies with specialization in Multimodal Information Applications; coursework in AI, computer vision, digital signal processing, and data engineering.