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Rahma S. Backend, AI and Machine Learning Platforms

My name is Rahma S. and I have over 1 years of experience in the tech industry. I specialize in the following technologies: Python, Flask, Machine Learning, Amazon SageMaker, node.js, etc.. I hold a degree in Bachelor of Technology (BTech). Some of the notable projects I’ve worked on include: Efficient-Detection: Transfer Learning-Based Deepfake Video Detection, TasteOfThebes API, LoRAview - AI interview assistant. I am based in Luxor, Egypt. I've successfully completed 3 projects while developing at Softaims.

I thrive on project diversity, possessing the adaptability to seamlessly transition between different technical stacks, industries, and team structures. This wide-ranging experience allows me to bring unique perspectives and proven solutions from one domain to another, significantly enhancing the problem-solving process.

I quickly become proficient in new technologies as required, focusing on delivering immediate, high-quality value. At Softaims, I leverage this adaptability to ensure project continuity and success, regardless of the evolving technical landscape.

My work philosophy centers on being a resilient and resourceful team member. I prioritize finding pragmatic, scalable solutions that not only meet the current needs but also provide a flexible foundation for future development and changes.

Main technologies

  • Backend, AI and Machine Learning Platforms

    1 year

  • Python

    1 Year

  • Flask

    1 Year

  • Machine Learning

    1 Year

Additional skills

Direct hire

Potentially possible

Previous Company

IBM Egypt

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Experience Highlights

Efficient-Detection: Transfer Learning-Based Deepfake Video Detection

Efficient-Detection is a deepfake video detection system designed to utilize transfer learning with EfficientNet models, making it suitable for deployment on systems with limited computational resources. By leveraging pre-trained EfficientNet models, Efficient-Detection achieves moderate accuracy in detecting manipulated video content while keeping computational requirements low.

TasteOfThebes API

This REST API provides a comprehensive database of restaurants in Luxor, focusing on both local and international cuisine. Our API offers: - Restaurant listings with detailed information - Menu items and pricing - Location data - Contact information The API serves as a centralized source of restaurant data, making it easier for developers to build applications that help visitors discover dining options in Luxor.

LoRAview - AI interview assistant

A command-line machine learning interview assistant built with a LoRA fine-tuned TinyLlama/Qwen model. This terminal-based app helps users practice ML interview questions by generating prompts, providing detailed answers, and reviewing candidate responses, making it an easy and effective way to prepare for interviews directly from the CLI. The model is intentionally not highly accurate, since it was created for learning purposes and trained with limited computational resources,

Education

  • Arab Academy for Science, Technology and Maritime Transport

    Bachelor of Technology (BTech) in Computer science

    2018-01-01-2022-01-01

Languages

  • Arabic
  • English

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