I’m Abdelrhman Ahmed Ezzat, an AI & Data Science graduate passionate about building end-to-end AI products.

My experience spans machine learning, backend engineering, modern frontend development, and real-time AI systems. Through projects such as VirtAI, I moved beyond standalone ML models into designing complete AI applications with scalable architectures, streaming APIs, and interactive user experiences.

I'm currently focused on becoming a well-rounded AI Engineer who can design, build, deploy, and maintain production-ready AI systems.

name       : Abdelrhman Ahmed Ezzat
location   : Kafr El-Sheikh, Egypt

education  : B.Sc. Artificial Intelligence & Data Science

role       : AI Engineer | Machine Learning Engineer
focus      : Full-Stack AI Systems & LLM Applications

specialties:
  - Machine Learning
  - Deep Learning
  - LLM Applications
  - Backend Engineering
  - Frontend Engineering
  - Real-Time AI Systems

currently_learning:
  - Agentic AI & RAG Workflows
  - Production AI Systems
  - Cloud Infrastructure & DevOps

engineering_mindset: |
  Build → Test → Debug → Optimize → Ship.
  Prefer simple, maintainable systems
  over unnecessary complexity.

Programming & Software Engineering
Data Engineering & Databases
Data Collection & Utilities
Data Analysis & Statistics
Data Visualization & BI
Machine Learning
Deep Learning & NLP
Computer Vision
Reinforcement Learning
Time Series & Forecasting
Backend & APIs
Frontend
MLOps & Deployment
Experiment Tracking

An end-to-end AI learning platform combining a real-time AI avatar, streaming LLM responses, and a modern interactive frontend.

Stack
Highlights	• Real-time AI avatar with streaming responses • Async FastAPI backend with WebSocket communication • Modern React + TypeScript frontend with 3D interactions • Fully Dockerized development & deployment workflow

• End-to-end AI applications with clean architecture
• Real-time AI systems with streaming APIs
• Modern React + TypeScript frontends
• FastAPI backend services with async workflows
• Deployable ML solutions with proper MLOps
• Production-ready systems that are easy to debug, test, and extend

• Stronger full-stack AI engineering
• Production ML & deployment habits
• LLM applications & agentic workflows
• Real-time & distributed systems
• Portfolio projects that behave like real products

• Build real products, not just demos
• Prefer clean code over clever code
• Keep systems simple, maintainable, and testable
• Learn through debugging and iteration
• Optimize only when necessary
• Deliver work that is both functional and polished