We can organize an interview with Aldin or any of our 25,000 available candidates within 48 hours. How would you like to proceed?
Schedule Interview NowMy name is Abdelrahman H. and I have over 1 year of experience in the tech industry. I specialize in the following technologies: Research & Development, Model Deployment, AI App Development, Full-Stack Development, Front-End Development, etc.. I hold a degree in , Master of Science (MS), Bachelor of Engineering (BEng). Some of the notable projects I've worked on include: Lightweight fine-tuning-Bert for sequence classification using PEFT, Automated Mammography Reporting through Image-to-Text Translation, Magnification Specific Breast Histopathology Image Classification, IBSR Brain Tissue Segmentation, QnA Chatbot implementation with semantic search and promt-engineering, etc.. I am based in Riyadh, Saudi Arabia. I've successfully completed 12 projects while developing at Softaims.
I approach every technical challenge with a mindset geared toward engineering excellence and robust solution architecture. I thrive on translating complex business requirements into elegant, efficient, and maintainable outputs. My expertise lies in diagnosing and optimizing system performance, ensuring that the deliverables are fast, reliable, and future-proof.
The core of my work involves adopting best practices and a disciplined methodology, focusing on meticulous planning and thorough verification. I believe that sustainable solution development requires discipline and a deep commitment to quality from inception to deployment. At Softaims, I leverage these skills daily to build resilient systems that stand the test of time.
I am dedicated to making a tangible difference in client success. I prioritize clear communication and transparency throughout the development lifecycle to ensure every deliverable exceeds expectations.
Main technologies
1 year
1 Year
1 Year
1 Year
Potentially possible
ScreenPoint Medical
The primary task of this project is to fine-tune BERT model usisng PEFT technique on an text-label dataset for the task of text classification. This techniques avoids training the model from scratch a
A vision-language implementation for automated mammography reporting using CLIP (Contrastive Language-Image Pre-Training) neural network. The project utilised an image-text paired datasets for trainin
This project was implemented for an image classification task for breast cancer into benign and malignant classes from eight different subclasses. The BreaKHis dataset is utilized in this project to c
Brain tissue (WM, GM, CSF) segmentation using both multi-atlas and nnUNet approaches. The project utilised both traditional and deep learning approaches to tackle the task and evaluate which approach
This project focuses on developing a custom chat bot designed to analyse and interact with users. The project initially explores datasets to find the most appropriate one for generating insightful and
in ML x Health summer school
2022-01-01-2022-01-01
Master of Science (MS) in Erasmus Mundus in Medical Imaging and Applications
2022-01-01-2024-01-01
Bachelor of Engineering (BEng) in Biomedical engineering
2017-01-01-2022-01-01