Computer Vision Architect

Computer Vision Architect

Design, train, and optimize deep learning models for computer vision tasks such as object detection, image recognition, segmentation, and video analytics. • Lead large-scale real-time implementations involving 100+ video streams. • Fine-tune models like CNNs and Vision Transformers using frameworks such as TensorFlow, PyTorch, and ONNX. • Build and deploy inference pipelines using NVIDIA technologies including NGC models, TAO Toolkit, DeepStream Metropolis, and Triton Server. • Apply pre- and post-processing techniques to enhance model performance and evaluation. • Develop and implement multi-object tracking algorithms across multiple camera feeds., Perform camera calibration and extract regions of interest (ROI), action recognition using temporal analysis techniques. • Develop models for feature extraction and come up with strategies for latent space analysis and manipulation of the data. • Proficiency in writing complex SQL queries, familiarity with NoSQL or Graph Databases (Neo4j) is an advantage. • Knowledge of infrastructure as code tools (e.g., Terraform, Ansible) is an advantage. • Proficiency with containerization technologies (Docker) and orchestration tools (Kubernetes) is an advantage.