Retail Buyer Segmentation
End-to-end ML platform combining K-Means clustering and six classifiers, achieving 99.55% accuracy with automated preprocessing and business analytics.
AI Engineer · Machine Learning Engineer · Data Scientist · Software Engineer
Passionate about building AI systems, machine learning applications, LLM-powered solutions, and data-driven products that solve real-world business problems.

I design, train, and ship machine learning systems — from classical models to modern LLM and RAG pipelines. Currently studying Computer Science & AI at Ain Shams University, I work at the intersection of research, data, and product.
I'm a Computer Science & AI student focused on turning research-grade models into production systems people actually use. My work spans computer vision, NLP, LLM applications, and full-stack ML platforms — with an emphasis on measurable business impact.
I care about clean data, honest evaluation, and thoughtful UX around model outputs. I move fluently between Python notebooks, PyTorch training loops, backend services, and container deployments.
From low-level model engineering to production-grade backends, visualization, and team leadership.
A blend of engineering, leadership, and community impact.
A mix of applied ML, deep learning research, LLM systems, and full-stack data products.
End-to-end ML platform combining K-Means clustering and six classifiers, achieving 99.55% accuracy with automated preprocessing and business analytics.
Advanced facial emotion recognition models achieving 74.8% Kaggle accuracy and 1st Place using Vision Transformers and deep learning.
Regression and classification pipelines for vitamin deficiency prediction using feature engineering and machine learning.
Machine learning models predicting patient survival using clinical datasets.
Rule-based medical diagnosis chatbot with a Streamlit interface.
Oracle database application for invoices, payments, customers, and transactions.
Marketing analytics dashboard providing insights into customer behavior and revenue.
Retrieval-Augmented Generation applications using embeddings, vector databases, and LLMs.
Place — Kaggle Emotion Detection Competition
Retail Buyer Segmentation Accuracy
Vision Transformer Accuracy
CNN Validation Accuracy
Faculty of Computer Science and Artificial Intelligence


NTI · Ministry of Communications and IT
View Certificate

Competitions, hackathons, IEEE, NASA Space Apps, UN Habitat, and conferences.
I'm open to full-time roles, internships, research collaborations, and freelance ML work.