Building intelligent systems with Deep Learning, Generative AI, Large Language Models, and Computer Vision.
I am an AI Engineer specialised in Data Science and Generative AI, with a Master’s degree in Data Science & Artificial Intelligence. My experience spans applied research and the development of AI solutions across diffusion models, large language models, RAG systems, computer vision and deep learning.
Sep 2024 — Jul 2026
ENSA — Chouaïb Doukkali University (UCD)
El Jadida, Morocco
Machine learning, deep learning, generative models, big data engineering and applied research, closed by a research internship in medical imaging.
Sep 2021 — Jul 2024
EST — Moulay Ismaïl University (UMI)
Meknès, Morocco
Completed after a University Diploma of Technology (DUT) in Artificial Intelligence & Emerging Technologies at the same school.
Sep 2021 — Feb 2023
Faculty of Letters and Human Sciences — Ibn Tofaïl University (UIT)
Kénitra, Morocco
Studied in parallel with the technology track, which is where the C1 level comes from.
Technologies I work with, grouped by area.
Languages I write production and research code in
From classical models to deep learning systems
LLMs, diffusion models and agentic systems
Getting models out of notebooks and keeping them running
Pipelines, stores and everything feeding the model
Protocols and infrastructure fundamentals
Working practices and communication
Five internships in morocco and abroad, where I learned to work in a team and to be autonomous.
CIAD Lab — UTBM
Mar 2026 — Aug 2026
Montbéliard, France
Medical Imaging & Generative AI
CDG
Nov 2025 — Feb 2026
Remote
LLM-based HR Virtual Assistant
Lalla Aicha Hospital
Apr 2024 — Jun 2024
Témara, Morocco
Multi-label thoracic pathology detection with CNN — benchmarked DL architectures on large-scale medical datasets and analysed model interpretability
Al Akhawayn University
May 2023 — Jul 2023
Ifrane, Morocco
Hyperconverged Infrastructure & Cloud
Al Akhawayn University
Jun 2022 — Jul 2022
Ifrane, Morocco
Internal Platform & Network Administration
Generative AI, computer vision, NLP, data science and MLOps. Each card opens into the full case study — the problem, the approach, the architecture, the dataset and what the numbers said.
Conditioning diffusion models with textual and spatial medical knowledge
Research project investigating how medical knowledge can be incorporated into diffusion-based image generation. The approach combines Stable Diffusion with LoRA fine-tuning and knowledge-guided conditioning using textual clinical information and spatial pathology annotations. Conducted as part of a Master's thesis at CIAD Lab, UTBM, France.
Hackathon prototype for multimodal breast cancer risk assessment
A hackathon prototype exploring multimodal breast cancer risk assessment by combining thermal imaging with structured clinical information. The system uses computer vision features from thermal images alongside clinical variables to produce a machine-learning-based risk score. 🏆 2nd Prize — MoroccoAI Summer School Hackathon 2025.
A conversational AI application that lets users chat with historical figures — Socrates, Cleopatra, Tesla, Rumi and others — powered by LLaMA 3.1 via the NVIDIA NIM API. Each figure is grounded in a detailed character profile built from their documented works, speech patterns and historical context.
A multi-agent prototype for automating parts of the data science workflow
A multi-agent system that orchestrates several stages of a data science workflow, from exploratory analysis and preprocessing to model training and explainability. Built as a prototype to demonstrate agentic automation of routine data science tasks.
AI-powered geospatial exploration of environmental indicators in Morocco
An AI-powered geospatial platform exploring the use of satellite imagery and Earth observation data for environmental monitoring in Morocco. Implements vegetation monitoring, water-resource tracking and land-cover analysis over time.
Vision-based produce recognition and weight estimation for retail checkout
A computer-vision checkout prototype that identifies loose produce from a camera frame and estimates its weight from visual information, then calculates an estimated price using configured produce prices. Built with MobileNetV2 and Gemini 2.5 Flash.
My work focuses on generative modelling and the reliability of AI systems: constraining generative models with domain knowledge, adapting foundation models efficiently, and evaluating language-model systems rigorously enough to deploy them in sensitive settings.
Constraining diffusion architectures with explicit domain knowledge, so that what they generate is plausible in the domain and not only in the pixel distribution. This is the subject of my current research at CIAD Lab.
Parameter-efficient fine-tuning, and going further — personalising generative models through external memory instead of gradient updates, so specialisation stops costing a training run.
Grounding, abstention, prompt injection resistance and reproducible evaluation — the difference between a language model demo and something an organisation can put in front of its employees.
Multi-agent architectures that carry out the routine part of the modelling cycle while leaving an auditable trail of why each decision was made.
Cloud, MLOps and generative AI certifications, plus the languages I work in day to day.
MLIAEdu
Model lifecycle, pipelines, deployment and monitoring
Oracle Cloud Infrastructure
End-to-end data science workflows on Oracle Cloud
Oracle Cloud Infrastructure
LLM services, RAG patterns and generative AI deployment
AWS Academy
Cloud operations, infrastructure support and reliability
AWS Academy
Core AWS services, architecture and security fundamentals
Mother tongue, including administrative and technical vocabulary.
Working language for research, documentation and technical writing.
Professional use in engineering and academic environments.
I am open to AI engineering roles, research collaborations and interesting problems in generative AI, computer vision and applied machine learning.