Software Engineer with strong technical foundation and a curious, forward-thinking mindset. Passionate about building seamless digital experiences and smart systems that solve real world problem.
2021–2025
National University of Sciences and Technology (NUST)
Courses:
C, C++, Java, Python
HTML5, CSS3, JavaScript
Dart and Flutter
Mysql, Firebase, MongoDB
Python, TensorFlow, Pytorch, Scikit-learn, OpenCV
Numpy, Pandas, Matplotlib
VS Code, Android Studio, Matlab, Jupyter Notebook, Postman
Git and GitHub
Figma and Canva
2024 - Nov 2024
Built cross-platform mobile apps with Firebase backend. Designed and developed the admin panel for Lawyer Digital Diary mobile app using Flutter, Dart, and Firebase, showcasing strong coding skills, problem-solving abilities, and real-time database integration. Created an intuitive and user-friendly interface for the SwiftHR mobile app, enhancing overall user experience.
2025-Present
Preprocessed large datasets, performed feature engineering, and fine-tuned machine learning models to enhance classification and prediction accuracy. Developed, optimized, and integrated machine learning models into applications, ensuring efficient real-time inferences.
Developed a MVP of an Errand-Running mobile app for a client. Implemented core features including task posting, runner matching, and manual payment handling, laying the groundwork for future premium services.
Developed an image description generator using CNN and LSTM trained on Flickr 8k Dataset.Implemented Tinker UI with real-time processing feedback, image preview capabilities, and a dual-panel layout that displays both the generated caption and detailed analysis
Developed a YOLOv8-based real-time driver drowsiness detection system. The system detects eye closure and yawning to identify potential drowsiness, providing visual and audio alerts to prevent accidents caused by driver fatigue.
Developed a minimalistic cross platform social media app. Implemented core functionalities such as user authentication, user profile, post creation, feed management, and real-time chat. Designed a user-friendly interface with engaging features.
Developed a computer vision based application for automated surface defect inspection using a fine-tuned MobileNetV2 model. System Classifies different defect types and provides visual feedback, predictive insights, and transparency through Grad-CAM.
Feel free to reach out for collaborations.
