Sumit Chouhan.

Engineering a multi-disciplinary AI and IoT pipeline and platform, deploying edge-based computer vision models and sensor networks to automate physical defect detection and streamline predictive maintenance workflows.

- Built automation pipelines for image generation using Midjourney, streamlining content creation workflows - Wrote Python scripts for web scraping, automating data collection and significantly reducing manual task time - Integrated Klaviyo with the backend to enable automated, personalized email notifications for account updates and user activity - Collaborated with the backend team to test and validate API integrations for correct implementation - Designed automation workflows using n8n to eliminate repetitive tasks, improving overall team efficiency

- Built and labeled a 1,800+ image multi-class dataset for aerial object detection and optimized YOLO models for edge inference, reducing latency by ~70% through parameter tuning. - Developed real-time object detection systems for aerial drones using YOLOv4-Tiny and other computer vision models, enabling efficient processing of live video feeds. - Created Python pipelines for data collection, annotation, training, and inference to support end-to-end computer vision workflows. - Collaborated with hardware and software teams to deploy computer vision solutions on edge devices, using Git for version control and team collaboration.