I am an AI and Computer Vision Engineer with hands-on experience designing production-grade image processing and generation workflows. I excel at building end-to-end vision pipelines, prompt-driven generation logic, and output validation to ensure reliability and reproducibility. I translate structured user input into controlled visuals that respect real-world constraints and downstream production needs, delivering consistent, high-quality outputs while enabling scalable deployment and collaboration across teams.…

EL MEHDI HICHAM

I am an AI and Computer Vision Engineer with hands-on experience designing production-grade image processing and generation workflows. I excel at building end-to-end vision pipelines, prompt-driven generation logic, and output validation to ensure reliability and reproducibility. I translate structured user input into controlled visuals that respect real-world constraints and downstream production needs, delivering consistent, high-quality outputs while enabling scalable deployment and collaboration across teams.…

Available to hire

I am an AI and Computer Vision Engineer with hands-on experience designing production-grade image processing and generation workflows. I excel at building end-to-end vision pipelines, prompt-driven generation logic, and output validation to ensure reliability and reproducibility.

I translate structured user input into controlled visuals that respect real-world constraints and downstream production needs, delivering consistent, high-quality outputs while enabling scalable deployment and collaboration across teams.

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Language

Arabic
Fluent
French
Advanced
English
Advanced

Work Experience

R&D Intern — AI & Computer Vision Engineer at SportScore
April 1, 2025 - October 1, 2025
Designed and maintained end-to-end AI pipelines for image and video processing in production-oriented environments. Implemented data preparation, normalization, and validation to ensure consistency across diverse visual formats and use cases. Built structured workflows converting high-level inputs into reliable, repeatable visual outputs. Enforced quality control through systematic evaluation, visual inspection, and metric-based validation. Optimized inference pipelines using GPU acceleration, profiling, and batching to improve throughput and stability. Documented workflows and experimental results to ensure reproducibility and long-term maintainability.
Machine Learning Intern — Vision & Data Pipelines at SETIME Laboratory, Ibn Tofail University
October 1, 2024 - January 1, 2025
Built structured pipelines for large-scale image and video datasets, including preprocessing, augmentation, and annotation checks. Applied deep learning models for visual understanding and feature extraction. Ensured dataset integrity, annotation quality, and experiment reproducibility. Conducted systematic evaluations and error analysis to improve output reliability.

Education

Master’s Degree in Artificial Intelligence at Ibn Tofail University
January 1, 2023 - January 1, 2025
Bachelor’s Degree in Physics at Ibn Tofail University
January 1, 2020 - January 1, 2023

Qualifications

Automation Testing for Python
November 1, 2023 - November 22, 2023
Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. This course explores the prominent frameworks for testing Python-based applications, how to implement tests using Pytest and PyCharm, and also Pytest test parameterization. Begin this 14-video course with a look at the concept of automated testing and classifying the different types of automation testing. Then view the prominent Python testing frameworks, and features of DocTest, Nose, and UnitTest that can be used to automate testing of Python-based applications. Discover how to implement unit testing in Pytest by using the PyCharm integrated development environment (IDE), and the procedure to parameterize tests by using Pytest. Learn about configuring Robot and executing Python tests using the Robot framework; build and test application programming interfaces (API) using Flask, and explore the process of testing APIs built in Flask using Postman. Also learn how to configure the Behave framework for Python testing by writing feature files. To conclude the course, learners observe how to automate testing of web components by using Selenium with Python.

Industry Experience

Computers & Electronics, Software & Internet, Media & Entertainment, Education, Professional Services
    paper 🔹 Automatisation de la Génération de Requêtes SQL via le Deep Learning

    Designed a system to translate natural language questions into SQL queries.
    Implemented NLP models based on Deep Learning to understand user intent and query structure.
    Trained and evaluated sequence-based models (LSTM / Transformers) for query generation.
    Validated generated queries on real database schemas.