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

We are seeking a highly skilled Computer Vision Engineer with hands-on experience in developing and deploying deep learning models for real-time image and video analytics. The ideal candidate will have a strong background in computer vision, deep learning frameworks, and edge deployment technologies.
* Python, Pytorch, tensorflow
* CV/ML model development
* Object recognition/Detection
* NVIDIA technologies
* MLOps