Computer Vision Engineer

Computer Vision Engineer Good knowledge on software configuration management systems • Strong business acumen, strategy and cross-industry thought leadership • Awareness of latest technologies and Industry trends • Logical thinking and problem solving skills along with an ability to collaborate • Two or three industry domain knowledge • Understanding of the financial processes for various types of projects and the various pricing models available • Client Interfacing skills • Knowledge of SDLC and agile methodologies • Project and Team management 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.