AI Engineer specializing in production-grade machine learning systems with deep expertise in Medical AI, Large Language Models, and MLOps. Proven track record of deploying 15+ AI solutions across healthcare, NLP, and computer vision domains.

🎓 Education: B.Sc. Electronics & Communications Engineering, Helwan University (2027)
📍 Location: Cairo, Egypt
☁️ Current Focus: AWS ML Certification | Advanced RAG Systems | Medical AI

Production medical AI system with explainable reasoning

🎯 Technical Achievements:

• ✅ Deployed multimodal AI combining text + medical images
• ✅ INT8 quantization: 75% model size reduction
• ✅ Intelligent doctor recommendation with specialty matching
• ✅ Patient follow-up with appointment tracking
• ✅ 10,000+ medicine database with vector search
• ✅ Sub-200ms inference for real-time clinical use
• ✅ Explainable AI with reasoning chains

💻 Technology Stack:

Core: PyTorch | Multimodal Transformers
Backend: FastAPI | ONNX Runtime
AI: RAG | Medical Imaging | NLP
Optimization: INT8 Quantization

Performance Metrics

Diagnosis Accuracy

Optimization

Speed Score

Privacy-first local LLM development assistant

🔥 Key Features:

• 🔒 100% offline processing (zero-latency)
• 🎨 Multimodal: code + images + documents
• 🤖 Context-aware workspace suggestions
• 👥 1,000+ daily active users
• ⭐ 95% user satisfaction rate
• 🚀 Full VS Code API integration

💻 Technology Stack:

Core: Python | VS Code Extension API
AI: LangChain | Local LLMs
Processing: Multimodal Analysis
Storage: Vector DB | RAG Systems

📂 View Repository →

User Metrics

Uptime

Satisfaction

Privacy

Hybrid abstractive/extractive summarization

📊 Research Achievements:

• 📈 25% ROUGE score improvement over baselines
• ⚡ 60% faster processing through distillation
• 🔄 Fine-tuned BART, T5, and PEGASUS models
• 📚 Multi-document with coherence optimization
• 🎯 Domain-specific dataset adaptation

💻 Technology Stack:

Models: BART | T5 | PEGASUS
Framework: Transformers | PyTorch
Techniques: Fine-tuning | Distillation
NLP: Tokenization | Domain Adaptation

📂 View Repository →

Performance Gains

ROUGE Score

Processing Speed

Clinical-grade ML classification

🏥 Medical AI Implementation:

• 🎯 97% accuracy, 95% recall
• 🔍 Ensemble: SVM + RF + LR
• 📊 Advanced feature selection (RFE, LASSO)
• ✅ Robust cross-validation
• 🏥 Clinical deployment ready

💻 Tech: Scikit-learn Ensemble Medical AI

📂 Repository →

Clinical Metrics

Accuracy

Recall

Precision

F1-Score

Enterprise-scale predictive modeling

📊 Data Engineering:

• 📈 5M+ flight records processed
• 🎯 85% prediction accuracy
• 🔧 +30% from feature engineering
• 📉 Interactive Power BI dashboard
• 🔍 Root cause analysis

💻 Tech: Python Time Series Power BI

📂 Repository →

Analytics Scale

Prediction

Data Coverage

Comprehensive NLP research framework

🧠 Implementation:

• 🎯 92% sentiment analysis accuracy
• 🔬 Benchmarked RNN, LSTM, GRU
• ⚡ Attention mechanisms integrated
• 📦 Production-ready pipeline
• 📚 Educational resources included

💻 Tech: PyTorch RNN LSTM GRU NLP

📂 Repository →

Model Performance

RNN

LSTM

GRU

+ Attention

Healthcare cost forecasting with interpretability

💡 Predictive Analytics:

• 📊 R² score: 0.87
• 🎯 Ensemble methods (RF, XGBoost, GB)
• 🔍 SHAP values for interpretability
• 🔧 Extensive feature engineering
• 💼 Insurance underwriting insights

💻 Tech: Scikit-learn XGBoost SHAP

📂 Repository →

Model Performance

R² Score

Interpretability