✨ AI Summary
We are looking for a Computer Vision Engineer with expertise in PyTorch and TensorRT. The role involves building and deploying high-performance AI models, including training detection, classification, and segmentation models, implementing multi-object tracking, and optimizing models for production using NVIDIA TensorRT and Docker. The engineer will write production-grade Python code with a focus on modularity and scalability, and containerize applications using Docker.
Ideal candidates will have 3+ years of experience in Computer Vision/Deep Learning, with strong skills in Python, PyTorch, and OpenCV. Experience with NVIDIA TensorRT and model optimization techniques like quantization and pruning is highly preferred. A solid grasp of software engineering principles, including Git, testing, and CI/CD, is also required. The ability to work on non-vision AI implementations is an added advantage.
Requirements
3+ years in CV/Deep Learning.Python, PyTorch, OpenCV.Strong preference for experience with NVIDIA TensorRT and model optimization (quantization/pruning).Solid grasp of software engineering principles (Git, testing, CI/CD).Can work on other non-vision AI implementations
Description
We are seeking a Computer Vision Engineer with strong software and AI fundamentals to build and deploy high-performance AI models. You will handle the full pipeline—from training detection and segmentation models to optimizing them for production using NVIDIA TensorRT and Docker.Core ResponsibilitiesModel Training: Train and fine-tune models for Detection, Classification, and Segmentation (e.g., YOLO, ResNet, U-Net).Tracking: Implement Multi-Object Tracking (MOT) algorithms for complex video streams.Engineering: Write production-grade Python code with a focus on modularity and scalability.Deployment: Containerize applications using Docker for consistent deployment.