- 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.