👋 Hello, my name is

Mohamed Rida
Ben Touhami

Data Engineer

🚀 Transforming data into intelligent insights with AI & Big Data

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About Me

Enthusiastic AI & Big Data Engineer, graduated from the National School of Applied Sciences of Al Hoceima, driven by a passion for IT, AI, and data. Eager to learn and contribute to challenging projects, I possess strong skills in artificial intelligence, big data engineering, and data science. Committed to continuous growth, I am excited about the opportunities to apply my knowledge and skills in real-world scenarios.

15+

Projects Completed

5+

Certifications

My Skills

Technologies and tools I work with

Big Data & BI Tools

Hadoop

Spark

Data Warehouse

Docker

Airflow

Kafka

Power BI

Web Scraping
Selenium, BS4

Databases

MySQL

Oracle (PL/SQL)

Cassandra

MongoDB

PostgreSQL

SQL Server

Machine Learning

Regression

Classification

Clustering

Dimensionality Reduction

Scikit-learn

Pandas

Languages & Frameworks

Python

Java

Spring Boot

React

HTML

CSS

JavaScript

OS

Windows

Ubuntu

Cloud Services

Azure Databricks

Azure Function

Azure Data Factory

Azure Data Lake

Languages

English (Advanced)

French (Advanced)

Arabic (Native)

Education

Academic background and qualifications

Master in Data Science

Claude Bernard University Lyon 1

Lyon, France

2025 - 2026

Advanced studies in Data Science, focusing on machine learning, deep learning, statistical analysis, and AI applications. Developing expertise in cutting-edge data science methodologies.

Data Engineering Degree

École Nationale des Sciences Appliquées d'Al Hoceima

Al Hoceima, Morocco

2020 - 2025

Specialized in Data Engineering with focus on data pipelines, machine learning, and distributed systems. Gained expertise in big data technologies, cloud computing, and data science.

Professional Experiences

Real-world impact through data engineering

Data Science and DevOps Internship at Digital Place (February - July 2025)

Vision Pipeline for Trailer Automation & Tracking

Designed and deployed an end-to-end computer vision pipeline for automated trailer detection, segmentation, and tracking. Trained YOLOv11 (97.8% accuracy) for license plate detection, EfficientNet-B2 for Moroccan/European plate classification, and integrated PaddleOCR for text extraction. Applied DINOv2 for visual embeddings enrichment. Integrated models into a FastAPI (backend) and React (frontend) application, containerized with Docker. Orchestrated CI/CD pipeline via Bitbucket Pipelines for automated build and continuous deployment on VPS.

Data Engineering Internship at Shiftbricks (July - September 2024)

Scalable Data Ingestion Pipeline for AI Applications

During my three-month internship at Shiftbricks, I designed and implemented a scalable data ingestion pipeline. The project involved extracting and transforming unstructured Arabic documents into structured formats using the Medallion Architecture. Automation was achieved using Apache Airflow. I utilized Python, PostgreSQL, and the FARM Stack.

Personal Projects

Building solutions with cutting-edge technologies

Big Data Real-Time Apartment Analytics with Kappa Architecture

Real-time data pipeline using Kappa Architecture for analyzing apartment listings. Implements streaming data processing with Apache Kafka, Spark, and ClickHouse. Features include real-time visualizations with FastAPI/React, materialized views for optimized queries, and HDFS data lake for storing apartment descriptions. Handles high-velocity data ingestion and provides interactive dashboards for market analysis.

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MLOps Pipeline for Telecom Customer Churn Prediction

End-to-end MLOps pipeline for customer churn prediction in telecommunications. Implemented real-time model monitoring in production using Evidently, Prometheus, and Grafana. Automated MLOps orchestration (retraining, evaluation, deployment) with MLflow and Airflow from customer data stored in Amazon RDS. Model deployed to production on Amazon S3, containerized with Docker, and deployed on an EC2 instance. Built a CI/CD pipeline to automate and secure the deployment of the application to EC2 instance.

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Designing a Big Data Architecture for an AI Trend Chatbot Based on RAG – Azure

End-to-end automated data pipeline that extracts daily news on AI trends. Leveraged Azure tools to orchestrate, process, and manage data ingestion, ensuring reliable and up-to-date content for the chatbot application.

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Interactive Sales Dashboard

Designed an interactive Power BI dashboard providing comprehensive sales insights. Features dynamic filtering by time, region, and metrics with visualizations for ordered quantities, invoiced sales, and product performance. Enables data-driven decision-making through performance comparison between categories and visual analysis of sales trends by region, client, product, and time period.

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Patent Analysis Project

Big data initiative focused on analyzing patent statistics in agriculture. Leveraged PySpark, MongoDB Atlas, PostgreSQL, and Power BI. Collected data from various sources including EPO APIs to aid stakeholders in assessing scientific competencies.

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Apartment Price Prediction

Real estate price prediction app featuring hybrid regression. Includes web scraping, EDA, and model optimization using StackingRegressor. Deployed as a web application with Flask, HTML, CSS, and JS.

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Exam Planification App

Robust application to streamline exam scheduling. Manages exam planning, room assignments, subjects, and courses. Features automated and manual scheduling and optimal room allocation.

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