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
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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

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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)

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Summary

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

ESP32-CAM Image Upload Server

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Summary

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

Real-Time IoT Systems

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Summary

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

Fake News Detection with DistilBERT

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Summary

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