AI / Machine Learning Engineer
Computer Science graduate building end-to-end ML pipelines, deep learning models and full-stack GenAI applications — grounded in strong software engineering fundamentals.
From classical ML pipelines to deep learning and generative AI, backed by solid software engineering practice.
Tools & Technologies
Hands-on production experience evaluating and improving AI/ML model behavior.
A mix of applied machine learning, generative AI, and software engineering system design.
2026 graduation project (team, Benha University) combining adaptive skill assessment, personalized learning paths, multi-agent code review, and RAG-based mentor feedback. Worked on IRT-based skill assessment and the FastAPI AI service powering code review.
View on GitHubFull-stack GenAI learning platform, developed as part of the Digital Egypt Pioneers Initiative (DEPI), helping users learn and self-evaluate English proficiency with adaptive, real-time language feedback via prompt-engineered API integrations.
View on GitHubEnd-to-end classification pipeline predicting banking outcomes — data preprocessing, feature engineering, and model tuning, benchmarking Random Forest, Decision Tree, and Logistic Regression to reach 91% accuracy.
View on GitHubDeep learning image classifier using Convolutional Neural Networks reaching a 75% baseline accuracy on the test set, improved through data augmentation and hyperparameter tuning.
View on GitHubAcademic foundation reinforced by self-directed learning and certified training.
Core coursework: Machine Learning, Deep Learning, Data Analysis, Algorithms & Data Structures, Software Engineering, Computer Vision, System Analysis. Self-directed: Machine Learning Specialization — Andrew Ng, DeepLearning.AI / Coursera.
Open to Machine Learning and AI Engineering opportunities — reach out any time.