Professional Overview
I am an AI Researcher and Machine Learning Engineer specializing in diagnostic deep learning, model validation, and end-to-end ML pipelines. With a strong foundation in Python, Scikit-Learn, and full-stack development, I focus on building robust, explainable AI systems. My technical track record includes developing NeuroLens, a diagnostic deep learning framework for early-stage Alzheimer's detection. This project achieved 93% accuracy and 100% recall on rare cases using Soft Voting Ensemble Learning and Grad-CAM for clinical interpretability. I also architected EvalGuard, an AI-powered debugging platform designed to identify silent machine learning bugs like data leakage, and implemented an unsupervised Anomaly Transformer model for financial fraud detection that achieved an ROC AUC score of 0.79. Currently serving as a Graduate Teaching Assistant in Artificial Intelligence, I mentor over 150 students in predictive modeling, data preprocessing, and evaluation metrics. I am looking to bring my combined expertise in research-backed AI systems, model interpretability, and full-stack engineering to build reliable, high-impact solutions.
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
data science & analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.