ML Engineer | NLP · Computer Vision · RAG — Computer Science graduate (AI & Data Science), Class of 2026, based in Egypt.
🟢 Open to AI/ML engineering roles and available for freelance work.
- Languages & Data: Python, SQL, Pandas, NumPy, Matplotlib, Seaborn
- ML / DL: PyTorch, scikit-learn, XGBoost, LightGBM, Hugging Face Transformers, YOLO (Ultralytics)
- NLP & RAG: LSTM / BiLSTM / CRF, Transformers, ChromaDB
- Deployment: FastAPI, Gradio, Streamlit
- Data Analytics: SQL Server, Power BI, Selenium
| Project | Description | Stack |
|---|---|---|
| rag-assistant-app | Local RAG assistant for AI research papers. Answers are grounded in 32 arXiv papers with paper and page citations; questions the documents can't answer are refused. | ChromaDB, FastAPI |
| AgriAdvisor | Crop recommendation (95.2% accuracy), yield prediction (R² = 0.97) and profit prediction (R² = 0.93) for Egyptian agriculture, served through a REST API and a 5-page web interface. | XGBoost, Random Forest, FastAPI, HTML/CSS/JS |
| object-detection-Road-Damage | Road damage detection comparing YOLOv8n, YOLOv10n and YOLO11n, with hyperparameter tuning and a Gradio demo. | YOLO, Gradio |
| oral-classifier | Oral disease image classification: custom ResCNN vs ResNet18 vs EfficientNet-B0 (85.7% test accuracy), with Grad-CAM explainability and a Streamlit demo. | PyTorch, Grad-CAM, Streamlit |
| named_entity_recognition-system | Named Entity Recognition on CoNLL-2003: LSTM, BiLSTM and BiLSTM+CRF built from scratch in PyTorch vs a fine-tuned DistilBERT (F1 0.891), evaluated with seqeval and deployed with a Gradio app. | PyTorch, Transformers, Gradio |
| Airbnb_Analytics | Airbnb market analysis across 10 European cities: web scraping, SQL Server data warehouse and Power BI dashboards. | Selenium, SQL Server, Power BI |
- B.Sc. Computer Science (AI & Data Science) — Arab Open University, 2026
- Applied AI Development – Level 2 — Information Technology Institute (ITI), 120 hours (Python for Applied AI, Computer Vision, YOLO, NLP, LLMs & Generative AI, RAG)
- Machine Learning Engineering Internship — NTI / ITIDA (120 hours)
- AI Diploma — Instant Software Solutions (170 hours)
- Deep Learning — NVIDIA
- PyTorch Image Segmentation and Support Vector Machines — Coursera