Professional Overview
Deep learning is a general purpose technology which can be shaped by the kind of data that is used to train. Since my main interest was in energy, chemistry and engineering, getting well labeled and big data was a challenge. Later on, I would decide to focus on medical data as a big chunk of the well labeled data comes from the medical sector. To improve my skills, I focused on training and validating classification, object detection and segmentation models for medical images. Although, am yet to win a medal publicly, I do have two competitions where I had solutions which won bronze models (ICR-Identifying Age-Related Conditions and UBC Ovarian Cancer Subtype Classification and Outlier Detection). The one lesson I have learned from Kaggle, is that ideas alone are worthless, speed and execution matters. This is one of the things that I have adopted into my workflow.
Areas of Expertise
Computer Vision
Develop image recognition, object detection, video analysis, and multimodal AI systems for real-world applications.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
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.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
