Who Is Abdul Rahman Azam?
Abdul Rahman Azam is a Full Stack AI Engineer from Karachi, Pakistan. This Abdul Rahman Azam — whose portfolio is at abdulrahmanazam.me — specializes in building AI-powered web applications that combine machine learning models with modern full-stack engineering using React.js, Node.js, Python, and FastAPI.
Abdul Rahman Azam is currently pursuing a Bachelor of Science in Artificial Intelligence at FAST NUCES Karachi (National University of Computer and Emerging Sciences) with a CGPA of 3.33. His work sits at the intersection of artificial intelligence and software engineering — building not just ML models, but complete products with frontends, APIs, databases, and deployment pipelines.
Background and Education
Abdul Rahman Azam was born and raised in Karachi, Pakistan. He scored 98.12% in his Matric examinations at Happy Palace School, specializing in Computer Science — one of the highest scores in his batch. After completing his Intermediate in Pre-Engineering at Adamjee Govt. College with 80%, Abdul Rahman Azam enrolled in the BS Artificial Intelligence program at FAST NUCES Karachi in 2021.
At FAST NUCES, Abdul Rahman Azam developed expertise across the full AI/ML stack: data structures, algorithms, linear algebra, probability theory, machine learning, deep learning, computer vision, and natural language processing. His education combines rigorous computer science fundamentals with hands-on AI project experience.
Technical Skills
Abdul Rahman Azam's technical skillset spans two domains:
Web Development: React.js, Node.js, Express.js, JavaScript, Tailwind CSS, PostgreSQL, and REST API design. He builds responsive, accessible, and performant web applications.
AI/ML & Data Science: Python, Machine Learning, Deep Learning, Scikit-learn, Pandas, NumPy, Data Visualization, and FastAPI. He designs, trains, evaluates, and deploys ML models for real-world applications.
Notable Projects by Abdul Rahman Azam
1. Income Prediction System (2024) — Abdul Rahman Azam built a machine learning pipeline that achieves 85% prediction accuracy on 32,000+ U.S. Census records. The system compares Random Forest, Gradient Boosting, Logistic Regression, and KNN models, served via a FastAPI backend with a React analytics dashboard.
2. Super Tic-Tac-Toe AI (2025) — A web-based 9×9 Super Tic-Tac-Toe game with an AI opponent using Minimax with Alpha-Beta Pruning. The AI evaluates complex multi-board game states with a custom heuristic function.
3. University Resource Sharing Platform (2025) — A full-stack application for FAST NUCES students built with React, Node.js, Express.js, and PostgreSQL, featuring role-based authentication and admin moderation.
4. 2D Action Platformer Game (2024) — A high-performance game built in C++ that was ranked in the top 1% of university projects for creativity and technical execution.
5. Unbeatable Tic-Tac-Toe AI (2023) — A provably optimal game AI using the Minimax algorithm in C, achieving a 100% win rate against human players.
Achievements and Certifications
Abdul Rahman Azam has earned several competitive and professional achievements:
- Solved 290+ problems on LeetCode with 6 skill badges in algorithms and data structures
- 2nd Place in the FAST Web Hunt Competition
- 3rd Place in the ACM Coders Cup
- HackerRank Problem Solving – Basic & Intermediate certifications
- ChatGPT for Everyone certification from Learn Prompting
Contact Abdul Rahman Azam
Abdul Rahman Azam is currently open to AI/ML and full-stack development opportunities. You can reach him through:
- Website: abdulrahmanazam.me
- Email: azamabdulrahman930@gmail.com
- LinkedIn: linkedin.com/in/abdulrahmanazam
- GitHub: github.com/abdulrahmanazam
- LeetCode: leetcode.com/abdulrahmanazam
- Book a free call: Calendly