Computer Vision Architect

Responsibilities :

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.