Muhammad Ammar Shahzad

PROFESSIONAL PROFILE Software Engineering undergraduate at NUST with a strong focus on artificial intelligence and machine learning, complemented by solid experience in full-stack and systems development. Skilled in building and deploying intelligent applications using PyTorch, TensorFlow, and modern web technologies, with hands-on research experience in adversarial learning and deep neural networks. Brings a strong mathematical foundation, practical problem-solving ability, and experience translating research and engineering concepts into scalable, real-world solutions. EDUCATION BESE School of Electrical Engineering and Computer Sciences (SEECS) , Islamabad , 3.5 (2026) INTERNSHIP EXPERIENCE GoSaaS Inc. 02-Jun-2025 - 13-Jul-2025 • Developed full-stack features for a containerized log management platform using Node.js, TypeScript, and Oracle JET, including a user settings module and secure OAuth 2.0 authentication. • Containerized the application stack using Docker and Docker Compose to streamline development and deployment workflows. • Implemented Redis-based notification queues to monitor log thresholds and trigger real-time alerts based on application logic. • Collaborated in Agile sprints, participated in daily stand-ups, and managed Git workflows including branching, rebasing, and conflict resolution. TUKL R&D Lab, National University of Sciences and Technology (NUST) 01-May-2024 - 16-Aug-2024 • Conducted research on adversarial attacks targeting deep neural networks, including CNNs and LSTMs. • Implemented and evaluated adversarial techniques such as FGSM and DeepFool to analyze model robustness. • Developed and trained machine learning models for speech classification and language translation tasks. • Built a text-to-speech pipeline converting textual input into audio output using deep learning techniques. Oz Limited 01-Jun-2023 - 18-Aug-2023 • Designed and implemented homepage recommendation systems using collaborative and content-based filtering techniques. • Applied supervised and unsupervised machine learning algorithms for user personalization. • Integrated trained machine learning models into an Android application for real-time recommendations. • Worked with cross-functional teams to align machine learning solutions with product requirements. FINAL YEAR PROJECT RentSight: AI-Powered Profitability Estimation for Short-Term Rental Properties This project aims to build a general-purpose AI-based application that estimates the profitability of residential properties when rented on short-term platforms like Airbnb. The system will use real estate data—scraped from websites like Zameen.com as a case study— and apply machine learning to predict expected income, occupancy rates, and investment potential. By analyzing factors such as location, price, and property features, the application will provide real estate investors with useful, data-driven insights into short-term rental profitability. This area is still emerging in Pakistan. Although Zameen.com will be used for testing and validation, the system will be scalable and adaptable for different real estate markets. The project holds both academic value and commercial potential.

Software Engineering undergraduate at NUST with a strong focus on artificial intelligence and machine learning, complemented by solid experience in full-stack and systems development. Skilled in building and deploying intelligent applications using PyTorch, TensorFlow, and modern web technologies, with hands-on research experience in adversarial learning and deep neural networks. Brings a strong mathematical foundation, practical problem-solving ability, and experience translating research and engineering concepts into scalable, real-world solutions.