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AI Engineer with less than a year in Machine Learning, Deep Learning, NLP, and Full-Stack Developmen
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Ingénieur informatique spécialisé en Data science et Intelligence Artificielle. Passionné par la conception et le déploiement de solutions intelligentes, avec de solides compétences en Machine Learning, Deep Learning, Traitement du Langage Naturel (NLP), LLM, RAG et computer vision
Esprim, Monastir
DIPLÔME D'INGÉNIEUR EN INFORMATIQUE · Informatique
N/A – Present
FSM, Monastir
LICENCE EN PHYSIQUE · Physique
N/A – Present
LSH
BACCALAURÉAT MATHÉMATHIQUES · Mathématiques
N/A – Present
READDLY- ReaddlyTech
Ingénieur Full-Stack spécialisé en IA Générative
September 1, 2025 – Present
Mahdia, Mahdia, Tunisia
La Paix - Medina Group
Ingénieur IA & Full Stack
August 1, 2025 – August 31, 2025
Hammamet, Nabeul, Tunisia
SafeSpeak
June 1, 2026 – Present
Application web full-stack de modération multilingue (FR/AR/Darija) détection de toxicité via texte/fichier/audio Dataset multilingue 22k+ (FR/AR/Darija) + pipeline NLP complet Fine-tuning DarijaBERT → 93.5% F1-macro Application Flask
Agent IA de Surveillance des Services Publics Tunisiens
June 1, 2026 – Present
Système autonome de veille et d'alerte (NLP+RAG+ Scraping + LLM) Scraping automatisé quotidien de +8 sources d'actualités tunisiennes NLP multilingue (fr/ar): NER custom, classification gravité, chunking sémantique RAG LangChain + Mistral-7B local: hybrid search, reranking, self-query retriever Génération automatique d'alertes contextualisées + carte interactive
Vocal Spectrum : Emotion Recognition from Speech
June 1, 2026 – Present
Application web full-stack + modèle audio Deep Learning Modèle CNN-LSTM + Attention → 94+ % accuracy reconnaissance 8 émotions (EmoDB + RAVDESS) Feature engineering: Log-Mel Spectrograms + MFCC + SpecAugment Application web React TS + shadcn/ui + Tailwind + TensorFlow.js Upload audio + enregistrement micro + visualisations Waveform/Spectrogram/Radar
Breast Cancer Diagnosis
June 1, 2026 – Present
Application web full-stack + Modèle ML interprétable Développement d'une application web interactive de diagnostic précoce du cancer du sein basée sur le dataset Wisconsin Breast Cancer (WDBC) Pipeline MLOps end-to-end Modèle SVM (RBF) → 98.2% accuracy Application web full-stack React + FastAPI
Scrum Fundamentals Certified
Unknown
June 1, 2026 – Present
Building RAG Agents with LLMs (NVIDIA DLI)
NVIDIA DLI
June 1, 2026 – Present
Building Transformer-Based Natural Language Processing Applications (NVIDIA DLI)
NVIDIA DLI
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a diverse range of applications for AI, from emotion recognition and public service monitoring to breast cancer diagnosis and content moderation. This breadth of interest and application suggests a curious and versatile individual. The internships, particularly the one focused on an intelligent visual learning platform for children with dyslexia, show a potential for impactful and socially conscious work. The combination of strong technical skills and diverse project experience indicates a good cultural fit for an innovative and problem-solving-oriented team.
Soft Skills & Operational Fit
The candidate's project descriptions highlight an ability to work on complex, multi-faceted problems, suggesting strong problem-solving skills. The full-stack nature of their projects implies a good understanding of end-to-end development, which is valuable for operational fit. The mention of CI/CD pipelines and microservices architecture indicates an appreciation for robust and scalable system design. However, without direct interview data, specific soft skills like teamwork, leadership, or adaptability cannot be fully assessed.