Ahmed Mohamed Gaber

Computer Science & AI Student | Backend & AI/ML Developer
Alexandria, EG.

About

Highly motivated Computer Science & AI student (graduating 2026) with robust foundational expertise in AI, NLP, and scalable backend development. Proven ability to fine-tune transformer models, architect Node.js-based solutions, and integrate IoT systems, including recent work on designing and deploying a 3PL logistics platform. Eager to leverage strong problem-solving and technical skills to contribute to innovative software engineering projects.

Education

Alexandria University
Alexandria, Alexandria, Egypt

B.Sc.

Computer Science & Artificial Intelligence

Skills

Programming Languages

Python, C++, JavaScript, SQL.

AI & Machine Learning

Machine Learning, Deep Learning, NLP, Transformers, LLMs, Word Embeddings, RNNs, LSTMs, GRUs.

Backend Development

Node.js, Express.js, REST APIs, Authentication & Security, JWT, OAuth, Microservices Architecture.

Databases & ORMs

MySQL, MongoDB, Sequelize, Mongoose.

Tools & Technologies

Git, Linux, Firebase, ESP32, IoT Sensors, HTTP, Deployment.

Soft Skills

Problem-solving, Communication, Fast Learner.

Projects

Emdadd 3PL Logistics Platform

Summary

Currently designing and developing the core architecture, authentication, database, and deployment strategy for a comprehensive 3PL logistics platform, integrating essential features for operational efficiency.

Sraha App Backend (Route Training Project)

Summary

Architected and developed a secure and scalable backend for a social messaging application during an intensive training program.

ESP32-CAM Image Upload Server

Summary

Developed and deployed a standalone HTTP server on an ESP32-CAM for remote image capture and upload functionalities.

Real-Time IoT Systems

Summary

Built and deployed real-time IoT systems, integrating microcontrollers with cloud services and physical sensors for interactive applications.

Fake News Detection with DistilBERT

Summary

Developed an AI solution leveraging transformer models to accurately classify news articles as authentic or fake.