Qurratulain Zafar

PROFESSIONAL PROFILE I am a final year Computer Science student with a strong focus on Machine Learning and Deep Learning. I have solidified my theoretical foundation by completing every AI elective offered during my undergraduate degree at NUST alongside a specialized Machine Learning and Deep Learning certification from Coursera. I have recently developed specialized expertise in Generative AI and building applications based on Large Language Models. My pre-medical background gives me a unique advantage in understanding the medical domain which allows me to deliver deep insights in Healthcare AI projects. This is demonstrated by my final year project ConfidMind where I engineered a privacy-preserving mental health AI agent using Federated Learning and RAG. Through these academic and personal projects I have honed my ability to work effectively in collaborative teams to deliver complex software solutions. I am proficient in the full development lifecycle which includes training complex models and deploying them as complete full-stack applications with robust backends. I am now seeking opportunities in the industry to work as an AI Engineer or Full Stack Developer where I can leverage my technical skills and collaborative experience to build scalable intelligent systems. EDUCATION BS Computer Science School of Electrical Engineering and Computer Science (SEECS) , Islamabad , 2.93/4.0 (2022) INTERNSHIP EXPERIENCE AI intern at ONT Lab (Optical Networks and Technologies Lab), NUST (Hybrid) 16-Jun-2025 - 18-Aug-2025 - Developed a context-aware AI chatbot for a network management application, enabling users to query system states and troubleshooting logs using natural language. - Architected a high-performance Redis database to serve as the primary knowledge store, optimizing data retrieval speeds to ensure low-latency responses for the chatbot. - Implemented a Retrieval-Augmented Generation (RAG) pipeline that dynamically fetched relevant technical documentation and network configurations from Redis to ground the LLM's answers. AI Intern at Apifiny (SMC Pvt) Ltd. 23-Jun-2025 - 25-Aug-2025 - Processed and cleaned historical financial market data using Python and Pandas to create high-quality datasets for model training. - Experimented with various machine learning algorithms using PyTorch and Scikit-learn to analyze trends in digital asset prices. - Assisted the engineering team in validating model performance by calculating various performance metrics. - Wrote and optimized Python scripts to automate data collection and feature engineering tasks for time-series analysis. - Contributed to the testing of inference endpoints to help ensure models functioned correctly before integration into the larger system. FINAL YEAR PROJECT ConfidMind: Federated Learning-Based Mental Health AI Agent - Developing a privacy-preserving machine learning architecture utilizing simulated federated learning to train emotion classifiers on decentralized data, ensuring sensitive user text remains on local clients. - Fine-tuning transformer-based models on the GoEmotions and DAIC-WOZ datasets to perform multi-task learning for real-time emotion detection and clinical depression screening. - Implementing a retrieval-augmented generation pipeline that uses vector databases to retrieve clinical knowledge, grounding generative AI responses to minimize hallucinations and ensure safety. - Engineering data pipelines to standardize heterogeneous input text and clinical labels into a unified format for joint multi-task training and evaluation. - Building a scalable inference API using FastAPI to serve real-time model predictions and integrating it with a React frontend to visualize emotional trends and progress reports. - Evaluating model performance using F1-scores for emotion classification and regression metrics for PHQ-8 score prediction

I am a final year Computer Science student with a strong focus on Machine Learning and Deep Learning. I have solidified my theoretical foundation by completing every AI elective offered during my undergraduate degree at NUST alongside a specialized Machine Learning and Deep Learning certification from Coursera. I have recently developed specialized expertise in Generative AI and building applications based on Large Language Models. My pre-medical background gives me a unique advantage in understanding the medical domain which allows me to deliver deep insights in Healthcare AI projects. This is demonstrated by my final year project ConfidMind where I engineered a privacy-preserving mental health AI agent using Federated Learning and RAG. Through these academic and personal projects I have honed my ability to work effectively in collaborative teams to deliver complex software solutions. I am proficient in the full development lifecycle which includes training complex models and deploying them as complete full-stack applications with robust backends. I am now seeking opportunities in the industry to work as an AI Engineer or Full Stack Developer where I can leverage my technical skills and collaborative experience to build scalable intelligent systems.