Role Overview

Develop and deploy computer vision solutions using deep learning, image processing, and modern CV frameworks. Build production-ready systems for visual understanding and analysis.

Responsibilities

Design and implement computer vision models for object detection, segmentation, and classification
Develop image processing pipelines for enhancement, filtering, and feature extraction
Build and optimize CNN architectures for various computer vision tasks
Implement CV solutions using OpenCV for real-time image and video processing
Train and fine-tune deep learning models using PyTorch for production deployment
Optimize models for performance, accuracy, and inference speed
Collaborate with cross-functional teams to integrate CV solutions into products

Requirements

Bachelor's degree in Computer Science, Electrical Engineering, AI/ML, or related field
Strong expertise in image processing techniques and algorithms
Proven experience with CNNs and deep learning architectures (ResNet, YOLO, U-Net, etc.)
Proficiency in OpenCV for computer vision applications
Hands-on experience with PyTorch for model development and training
Strong Python programming skills
Understanding of computer vision fundamentals (feature detection, image transforms, edge detection)

Preferred

Experience with object detection frameworks (YOLO, Faster R-CNN, SSD)
Knowledge of semantic/instance segmentation techniques
Familiarity with vision transformers (ViT, DETR, SAM)
Experience with video processing and tracking algorithms
Understanding of 3D vision, depth estimation, or SLAM
Knowledge of model optimization (TensorRT, ONNX, quantization)
Experience with GPU programming and CUDA
Familiarity with cloud deployment and MLOps

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Computer Vision Engineer