I build intelligent systems that bridge the gap between cutting-edge AI research and real-world impact. From multi-agent architectures to production-grade ML pipelines, I turn complex problems into elegant solutions.
I'm a passionate AI Engineer and Machine Learning Developer currently pursuing my Bachelor's in Computer Science at Ain Shams University. With a strong foundation in building production-ready AI systems, I specialize in creating intelligent multi-agent architectures, knowledge graphs, and scalable ML pipelines.
Currently working at Wider, a multinational company, where I build and deploy backend AI agent infrastructure handling production authentication flows and semantic metadata enrichment using LLM extraction pipelines.
As Head of AI at iCLUB, I lead strategic AI initiatives, organize workshops on generative AI and model deployment, and mentor the next generation of AI developers.
GPA: 3.4 / 4.0 (Very Good)
Grade: 99.3% | 30 Hours | AI Agents, LLMs, LangChain, RAG, Multi-Agent Systems
Multi-agent LangGraph pipeline (Extractor → Investigator → Resolver → Explainer) for automated claim triage and resolution with semantic deduplication using pgvector HNSW indices and multilingual embeddings.
Automated chatbot integrating multiple flight and hotel reservation APIs for real-time booking with workflow automation pipelines using n8n.
Desktop application for creating and managing exams with automated grading, user authentication, and performance tracking using OOP principles.
Compared ResNet50 (81.9%) vs EfficientNetB0 (96.3%) on NWPU-RESISC45 dataset for multi-class land cover detection using transfer learning.
Multi-class classification using XGBoost achieving 86.6% accuracy with engineered interaction features for improved predictive performance.
Time series forecasting with LightGBM predicting weekly sales (MAE ≈ $7,277) using rolling averages and TimeSeriesSplit cross-validation.
Applied SMOTE balancing and trained Logistic Regression, SVM, and Random Forest achieving 76% accuracy for diabetes prediction.
Segmented customers into 5 clusters via Elbow method with K-Means, visualizing spending vs. income patterns.
I'm always interested in hearing about new opportunities, collaborations, or just having a great conversation about AI and technology.