Hammad Hassan

I’m Muhammad Hammad Hassan , a computer science student with a strong passion for artificial intelligence, machine learning, and software development. My work focuses on creating innovative solutions that bridge the gap between cutting-edge technology and real-world applications.

My primary areas of interest include AI-powered systems, machine learning algorithms, natural language processing, and computer vision. I’m deeply committed to advancing research in these fields and exploring their potential to solve complex problems across various domains.

Currently, I'm focused on developing AI-powered systems and exploring their practical applications in real-world scenarios. I am passionate about advancing research in AI and machine learning to address challenging problems in various industries.

Sep 2024 — Feb 2025

Machine Learning Engineer
• Hirenze

Built an AI recruitment system for better candidate selection with adaptive questioning and unbiased evaluation. Developed real-time video monitoring with computer vision for accurate fraud detection during interviews. Integrated text-to-speech and speech-to-text modules, enabling seamless audio interaction for technical interviews.

AI Systems

Computer Vision

Text-to-Speech

Speech-to-Text

Python

Recruitment Tech

Jul 2023 — Sep 2023

Software Development Engineer Intern
• Devsinc

Developed a web application using Ruby on Rails with features like user authentication and RESTful APIs. Practiced software development best practices including MVC, Git, and agile workflows. Collaborated on code reviews and debugging to ensure clean, optimized code.

Ruby on Rails

Web Development

REST APIs

MVC

Git

Agile

Sep 2022 — May 2023

Front End Development Intern
• Bytewise Limited

Developed interactive web apps like Star Rating, Book List, and Weather App using HTML, CSS, and JavaScript. Built responsive UI components and handled DOM manipulation to enhance user experience. Gained hands-on experience in front-end logic, event handling, and browser-based app development.

JavaScript

HTML

CSS

DOM Manipulation

Responsive Design

Mar 2021 — Sep 2021

Software Engineer Intern
• Fareed Soft

Delivered a POS solution in 30 days, boosting client transaction efficiency by 25%. Built a desktop app with advanced analytics and reporting to streamline data visualization. Processed 10,000+ records through the app, accelerating insight generation for end-users.

Desktop Apps

Analytics

Data Visualization

Efficiency Optimization

Reach out for Full Resume

Apr 2023 — Jul 2023

Teaching Assistant
CS-162 Object Oriented Programming
• University of Engineering and Technology Lahore

Guided students in understanding OOP concepts including classes, inheritance, polymorphism, and encapsulation. Helped design coding exercises and reviewed C# assignments. Facilitated group discussions and labs for hands-on learning.

OOP

Teaching

Student Mentorship

Lab Facilitation

Dec 2022 — Mar 2023

Teaching Assistant
CS-161 Programming Fundamentals
• University of Engineering and Technology Lahore

Assisted in delivering programming fundamentals like variables, loops, and functions to 100+ students. Conducted remedial sessions and one-on-one guidance. Supported instructors with grading, lesson planning, and monitoring student participation.

Programming Basics

Python/C

Grading

Lesson Planning

Mentorship

Reach out for Full Resume

Telco Customer Churn

Developed a predictive model to forecast customer churn using demographic and usage data. Analyzed and visualized data to understand customer behavior and enhance retention strategies.

Prepmate - IELTS Writing Assistant

AI-powered IELTS Writing Task Support System using RAG and LangChain, delivering personalized feedback and online search integration to improve learning efficiency.

MotionArt

Built a digital art platform with real-time hand landmark detection for mid-air drawing using OpenCV and MediaPipe, achieving 86% detection accuracy.

Document Classification with Graph Features

Applied KNN and Graph Bijection to classify multi-label documents from web datasets with 90% accuracy and strong outlier resistance.