Computer Vision Engineer

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. 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