Full-Stack Web Developer
Bakchich
Working full-time as a full-stack web developer at Bakchich, a Tunisian industry-leading crowdfunding solution supporting content creators. Working on client-facing features like the stream-alerts service, and implementing workflows using Microsoft Azure resources, and migrating from a monolithic PHP project to a monorepo running Next.js apps.
Data Science Intern
keyrus
For my end of study internship, I worked on a Data Science project with the KEYRUS team. The project was about creating a solution for information extraction from images of identification documents and telecommunications fraud detection. Finaly, I created a mobile app that will allow the user to set up peoples' accounts and verify their identities and documents automatically.
Data Analysis Intern
REDNAKS-FENIX/WILDKARD
Started with working on multiple bots scraping public data organizing it into sheets for the marketing team and then moved onto creating a dashboard using Google Analytics data of their E-commerce website.
Frontend / Full-stack Developer
AIESEC Medina LST
Learning web development hands-on. Worked on an PWA for internal use and a landing page for AIESEC Medina.
Producer
INTERFERENCE
My first experience in light art, and I haven't stopped since. Member of the production team taking care of every technical aspect behind the great artworks displayed as well as inventory management. Working alongside artists from all around the globe where communication is key to turning their vision into reality.
Symfony | Stimulus | TailwindCSS | DALL•E | API | ExpressJs | Supabase | JS
We Created a web interface making use of DALL•E Text-To-Image ML model. We used Supabase to hold the database of users and images they generated. We created an API using ExpressJs to send Auth emails to the user when needed. We also Used tailwindCSS for the styling.
Python | Beautiful Soup | ML | NLP
The Task is to scrape movie data off of IMDB and conduct sentiment analysis on it. We used Beautiful Soup to scrape data into two datasets one containing the general data of every movie and the other one contains every review from a selected set of movies which we then use to perform the sentiment analysis using two models 'Multinomial Naïve Bayes' and 'VADER'