senior-computer-vision

Build a production computer vision system by training models on local image data, optimizing inference pipelines, and implementing scalable architecture. This approach handles real-time object detection and video analysis for real-world applications.

Yes, OpenCV and YOLO support real-time object detection in video analysis tools. This combination enables state-of-the-art visual AI processing to track assets and identify defects within video feeds.

Optimize PyTorch inference pipelines for computer vision through performance tuning, scalable architecture design, and distributed computing. This ensures production-grade AI systems maintain efficiency during real-time visual processing and object detection.

Yes, this approach supports vision transformers and diffusion models for image processing. Expertise spans these advanced architectures alongside 3D vision and segmentation models to enable comprehensive visual AI development.

Deploy MLOps for scalable computer vision systems by implementing distributed computing, performance optimization, and scalable architecture. This infrastructure supports continuous model training and real-time inference for production environments.