• 🔭 Results-driven AI & Machine Learning Engineer with a solid foundation in Computer Vision, NLP, and Deep Learning architectures.
• 🌱 Proven ability to architect end-to-end AI pipelines, from training custom models to deploying production-ready RESTful APIs using FastAPI and Docker.
• ⚡ Adept at bridging traditional Machine Learning with modern Generative AI, leveraging tools like LangChain and LangGraph to build stateful, multi-agent workflows.
• 🎯 Passionate about solving complex business challenges through scalable, data-driven solutions and continuous technical growth.

Work Experience

• AI and Machine Learning Intern @ AMIT Learning (June 2023 - February 2024)
  • Collaborated with senior engineers to develop and deploy Machine Learning and Deep Learning models using Python and TensorFlow.
  • Gained hands-on experience in Computer Vision, NLP, and MLOps, actively contributing to solving real-world data challenges.
• Military Service @ Egyptian Armed Forces (Completed: Feb 2025 - Mar 2026)
  • Honorably discharged, demonstrating discipline, adaptability, and teamwork in high-pressure environments.

Education

• Modern Academy For Computer Science And Information Technology
  • Bachelor of Computer Science | Graduation Year: 2024 | Grade: 3.18/4.00

Enterprise & Generative AI

• Omni Enterprise AI Agent: Architected a stateful AI agent using a custom manual ReAct loop (LangChain Core) and in-memory session management. Deployed via FastAPI with strict Pydantic validation, leveraging Pure Python and Ollama (Qwen 2.5).
• Smart Document Q&A System (Local RAG Engine): Architected and developed a privacy-first RAG pipeline using FastAPI, ChromaDB, and Ollama to enable semantic search over proprietary PDF documents.
• Enterprise AI Automation Suite: Architected 19+ production-grade agentic workflows using n8n to automate complex business operations.

Computer Vision & Deep Learning

• Psoriasis Disease Classification: Engineered and containerized a production-ready RESTful API using FastAPI and Docker to serve a hybrid CNN/ViT computer vision model. Optimized model inference by converting legacy Keras models to TensorFlow Lite.
• Image Caption Generation: Developed an automated image captioning model achieving 98% accuracy using CNNs for feature extraction and LSTMs/Transformers for sequence generation.
• Breast Cancer Detection: Built a robust early detection system using Computer Vision and CNN architectures with advanced data preprocessing.

NLP & Data Science

• Text Summarization: Implemented a summarization tool utilizing advanced transformer models (T5) to condense lengthy documents efficiently.
• Music Recommendation System: Designed an NLP-based recommendation engine using TF-IDF vectorization and Cosine Similarity, integrated with Spotify API via Streamlit.

Languages:

AI, ML & Data Science:

Backend & Databases:

Cloud & Deployment:

Tools & Others:

• Deep Learning Applications for Computer Vision – University of Colorado Boulder (Mar 2024)
• Deep Learning Fundamentals – DeepLearning.AI (Jan 2024)
• Introduction to Computer Vision and Image Processing – IBM (Jan 2024)
• Associate Data Scientist in Python – DataCamp (Dec 2023)
• Data Analyst with Python – DataCamp (Dec 2023)