
Ahmed Ali
Software Engineer (Backend)
I'm a software engineer focused on backend development. I have strong problem-solving skills and hands-on experience with a wide range of technologies.
Education
2016 - 2020
Aswan, Egypt
Bachelor of computing and information technology, Computer Science
Arab cademy for Science, Technology and Maritime Transport (AAST)
Achievements:
- GPA: 3.2/4.0
- Teaching structured programming with C/C++, problem solving techniques to prepare student for the ACM programming contests
Professional Experience
- Systems design.
- Developed and maintained APIs for web platforms and mobile applications.
- Integration with many services to raise and achieve the efficiency of applications.
API Design Node.js Nest.js PostgreSQL Redis REST
- Worked in a team specialized in developing and implementing Metering Data Management System (MDMS), Head-End System, and other AMI Metering software solutions.
- Ensured interoperability between software and Smart Metering components in collaboration with meter manufacturing R&D teams.
- Developed a high-performance, scalable Meter Data Acquisition System (MDAS) under Oracle Linux using asynchronous services.
Node.js C++ Redis NoSQL Oracle Linux Load Balancing Asynchronous Services
- Systems design.
- Developed and maintained APIs for web platforms and mobile applications.
- Integration with many services to raise and achieve the efficiency of applications.
API Design Nest.js Express REST MySQL Swagger
- Developed features to enhance the user experience.
- Sites APIs development and maintenance.
- Integration with many services to raise and achieve the efficiency of the application.
API Design Node.js Express REST MongoDB Swagger
Technical Skills
Programming Languages
Frameworks & Libraries
Databases
DevOps & Cloud
Other Skills
Achievements
Graduation Project
Emotion Recognition using facial expressions
Institution
Arab Academy for Science, Technology & Maritime Transport (AAST)
Year
2019
Supervisor
Ass.Prof.Dr/ Yasser Omar, Professor of Computer Science
Project Description
This project uses a conventional neural network, functional model,and residual modules to detect human faces and Classify face emotion.
Key Highlights
- Designed and trained a CNN model achieving 90% accuracy on emotion classification
- Implemented real-time facial expression detection using OpenCV
- Optimized model performance to run efficiently on limited hardware
- Prepared comprehensive documentation and presented project results to faculty
Technologies
Deep learning Python CNN