Applied AI Engineer - Building Computer Vision Systems, ML Pipelines & Intelligent Automation
I build applied AI systems focused on computer vision, machine learning, and intelligent
automation. My work involves designing end-to-end pipelines - from data processing and model
training to deployment using Docker and cloud platforms.
I'm particularly interested in AI systems that interact with the real world - vision-based
monitoring, intelligent automation, and decision-support tools.
AI-based environmental monitoring system that analyzes real-time air quality data, visualizes AQI trends, and enables environmental policy simulation to support decision-making around urban air quality.
Computer vision system that automatically detects filled bubbles from scanned OMR answer sheets and calculates exam scores — eliminating manual checking with image processing pipelines.
Computer vision project that classifies food items from images using deep learning, with planned extensions toward automated nutritional estimation and food analytics.
Real-time computer vision pipeline detecting mask compliance using CNN-based image classification. Designed for integration with surveillance or public safety monitoring systems.
AI-based chatbot prototype that answers financial queries and provides simple financial insights using natural language processing techniques.
Worked on AI-based video analytics systems focused on driver monitoring and alarm validation. This involved
building computer vision pipelines to analyze video frames and detect unsafe driving behaviors such as
fatigue, distraction, and non-compliance.
This experience provided exposure to real-world AI system development, including data processing, model
evaluation, video processing pipelines, and system-level integration challenges.