Ishant Bansal

Led development and production deployment of GenAI and computer vision solutions for pharma, delivering compliance-checking pipelines and document assistant chatbots used by sales, regulatory, and medical affairs teams; reduced external vendor reliance for campaign creation and improved compliance accuracy with interpretable image-analysis pipelines.

Developed smart-city and smart-warehouse computer vision systems (ALPR, intrusion detection, inventory analysis) and deployed optimized models on edge devices using TensorRT and DeepStream, improving inference speed and memory usage.

Built smart-city solutions including vehicle detection, tracking and accident detection, and optimized deep learning models for deployment using OpenVINO and TensorRT while integrating Kubernetes and Kafka for scalable pipelines.

Completed a Bachelor of Technology in Computer Science with coursework and projects focused on computer vision, machine learning, and software engineering.

Completed Higher Secondary Education (Class XII).

Completed Secondary Education (Class X).

Completed a Computer Vision Nanodegree and received the PyTorch Scholarship by Facebook (Udacity) focusing on practical deep learning and vision projects.

Completed the Deep Learning Specialization and Machine Learning courses covering neural networks, optimization, and applied ML techniques.

Completed scholarship coursework in front-end development.