---
title: "Abhishek Chakraborty - Computer Vision Engineer in Greater Hyderabad Area | Tal"
description: "Meet Abhishek Chakraborty, Computer Vision Engineer | Real-Time Video Analytics | YOLO • RT-DETR • PyTorch • OpenCV | TensorRT • NVIDIA Triton | Edge AI amp; Deep Learning based in Greater Hyderabad Area. AI stack includes claude code, deepseek, and gemini. Explore public work links including githu..."
image: "https://tal.club/meet/abhishek-chakraborty-2/share-image"
---

# Abhishek Chakraborty on Tal

## Headline

Computer Vision Engineer \| Real\-Time Video Analytics \| YOLO • RT\-DETR • PyTorch • OpenCV \| TensorRT • NVIDIA Triton \| Edge AI amp; Deep Learning

## About

Computer Vision Engineer with hands\-on experience in Python, OpenCV, PyTorch, TensorFlow, YOLO, RT\-DETR, ONNX, TensorRT, and Edge AI\. Experienced in building and deploying real\-time computer vision solutions for object detection, tracking, video analytics, and image processing\. At ZestIoT, worked on aviation video analytics using YOLO, RT\-DETR, ByteTrack, and real\-time inference pipelines, along with model optimization and deployment using TensorRT and NVIDIA Triton\. Also gained experience at IIT Bombay and through other ML projects involving Raspberry Pi, NVIDIA Jetson, and edge AI deployment\.

## Location

Greater Hyderabad Area

## AI stack

claude code

deepseek

gemini

github copilot

inception

opencode

## Work experience

Computer Vision Engineer at ZestIoT Technologies, Dec 2025 \- Sep 2026 · 10 mos, Hyderabad, Worked on real\-time computer vision and video analytics solutions for airport and aviation operations\.• Developed and optimized object detection and tracking pipelines using Python, PyTorch, YOLO, RT\-DETR, OpenCV, and ByteTrack\.• Worked on passenger and aircraft turnaround analytics, including boarding/deboarding events and real\-time video\-based monitoring\.• Worked with ONNX, TensorRT, and NVIDIA Triton for optimized model inference and deployment\.• Worked with edge and GPU\-based environments, focusing on real\-time inference, performance optimization, and reliable CV pipelines\.

Computer Vision Engineer at ZestOpX \- the new chapter of ZestIoT, Dec 2025 \- Sep 2026 · 10 mos, Built and improved real\-time computer vision systems used for airport turnaround and baggage\-handling operations\. Built a passenger\-counting pipeline using YOLOv7 and ByteTrack, using appearance, movement, and tracking information to distinguish crew from passengers\. Fine\-tuned and tested YOLOv12\-s and RT\-DETRv2 models and compared their performance with YOLOv7 using ONNX and TensorRT\. Built an optical\-flow\-based system for baggage loading and unloading detection at airport sites in Lyon\. Developed detection logic for aircraft events such as On\-Block, Off\-Block, and belt\-loader activities, using Bi\-LSTM and ROI\-based methods to improve detection under camera movement and other visual changes\. Investigated false positives, missed detections, and unstable confidence scores using system logs and video data; monitored model performance \(confidence, latency, resource usage\)\. Configured Milestone VMS for multi\-camera video integration in airport projects\.

Machine Learning Engineer at BaaVilbuch, 2024 \- 2024 · Less than a year, • Developed a DCGAN\-based image inpainting system for image restoration, improving the model through architecture changes and data augmentation\. • Exported the trained model to ONNX for faster inference and evaluated image quality using SSIM against traditional inpainting methods\.

Machine Learning Engineer at IIT Bombay, 2022 \- 2023 · 1 yr, • Built a monkey detection system for Raspberry Pi\-compatible hardware: evaluated YOLOv3\-Tiny, YOLOv5, EfficientDet\-D0 and EfficientDet\-D1, selected EfficientDet\-D0, and applied quantization to achieve over 6 FPS on a single thread\. • Collected and annotated images, trained with TensorFlow Model Maker, and evaluated detections using IoU\. Developed a modular model handler and Raspberry Pi 4 integration with camera capture, GPIO, system\-health monitoring and Telegram notifications; used multi\-frame aggregation to reduce false positives\. • Developed an active\-learning demo using VGG16/ResNet50 to identify uncertain unlabeled images for human annotation and iterative retraining\. • Built an Aadhaar masking pipeline using PDF\-to\-image conversion, OpenCV contour detection, CNN\-based card detection, OCR, and blurring of the first eight digits\.

Machine Learning Engineer at Blurgs, 2022 \- 2022 · Less than a year, • Deployed YOLO\-based object detection on NVIDIA Jetson Nano for real\-time applications, reducing false detections by around 15% through model and threshold improvements\. • Developed an automated box detection system for ITC\.

## Education

Bachelor of Commerce, The Bhawanipur Education Society College

## Public links

- github: https://github\.com/Abhishek183422

[View the original profile](https://tal.club/meet/abhishek-chakraborty-2)

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