✨ AI Summary
We are seeking a Computer Vision Engineer to build and deploy high-performance AI models. Responsibilities include training and fine-tuning detection, classification, and segmentation models (e.g., YOLO, ResNet, U-Net), implementing multi-object tracking for complex video streams, and delivering production-grade Python code. The role also involves deploying applications via Docker and optimizing models with NVIDIA TensorRT. A strong foundation in CV/deep learning, experience with Python and PyTorch, and hands-on expertise in model optimization (quantization/pruning) and software engineering practices (Git, testing, CI/CD) are required.
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.