Hasnain Ali

PROFESSIONAL PROFILE Machine Learning Engineer with hands-on experience building, fine-tuning, and deploying machine learning models, including modern large language models (LLMs). Strong foundation in Python, core machine learning algorithms, and NLP, with practical experience implementing automated data preprocessing, training, and inference pipelines. Worked on model evaluation, prompt optimization, and LLM fine-tuning for task-specific use cases, with exposure to deploying models through APIs and cloud-based environments. EDUCATION Software Engineering School of Electrical Engineering and Computer Sciences , Islamabad , 2.75 (2026) INTERNSHIP EXPERIENCE Optical Network and Technologies Labs 16-Jun-2025 - 01-Sep-2025 As an ML Intern at ONT Lab , I worked on OTDR measurement data to estimate Quality of Transmission (QoT) and detect impairments in optical networks. I implemented machine learning(transformers) models, conducted extensive data preprocessing on large optical trace datasets(time series datasets), and applied explainable AI techniques, including SHAP-based feature attribution, to interpret model decisions and identify key physical parameters affecting QoT. FINAL YEAR PROJECT Emotion Detection form Micro Expressions Aimed to enable accurate recognition of genuine human emotions by analyzing facial micro-expressions using a lightweight, production-ready deep learning pipeline. Developed an emotion recognition system that detects facial micro-expressions that are brief, involuntary cues often missed by traditional models. Implemented a teacher–student architecture using self-supervised learning for motion representation and knowledge distillation to train a lightweight, deployment-ready model. Secure Encrypted Decision Tree Inference System Built a client–server system using fully homomorphic encryption for decision tree inference to enable privacy‑preserving classification where the server never sees client plaintext data Smart Consent Form Desk Assistant Built an LLM-powered extraction pipeline using Llama-3.1-8b-instant Model hosted by Groq and prompt-engineered JSON schemas to automatically structure key clinical and consent fields from unstructured healthcare documents TECHNICAL EXPERTISE Large Language Models Fine Tuning LLMs (LoRA, PEFT), Prompt Engineering, Embedding Generation, Semantic Search, Vector Databases (FAISS, Chroma), RAG, Text Classification, Information Extraction, Sequence-to-Sequence Modeling, Generative AI, Open-Source LLMs (LLaMA, Mistral, Phi) Programming and Software Foundations Python, PyTorch, TensorFlow, NumPy, Pandas, Scikit-learn, OOP, DSA, SQL, NoSQL, Database Management,, Version Control

Machine Learning Engineer with hands-on experience building, fine-tuning, and deploying machine learning models, including modern large language models (LLMs). Strong foundation in Python, core machine learning algorithms, and NLP, with practical experience implementing automated data preprocessing, training, and inference pipelines. Worked on model evaluation, prompt optimization, and LLM fine-tuning for task-specific use cases, with exposure to deploying models through APIs and cloud-based environments.