Computer Vision Engineer

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.

Additional Responsibilities:

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