Computer Vision Engineer

Roles and Responsibilities of a Computer Vision Engineer (AI):

1. Collaborate actively with the Computer Vision and Deep Learning Team to train Computer Vision Models effectively.

2. Coordinate closely with the Data Science Team to curate appropriate datasets for training purposes.

3. Tackle challenging problem statements to fine-tune models using extensive datasets.

4. Implement State-of-the-Art (SOTA) architectures for model training purposes. Work closely with the Research and Development (R&D) Team to enhance model accuracy and precision, particularly for CCTV cameras.

Requirements:

1. Proficiency in training Detection, Classification, and Segmentation Models using TensorFlow/PyTorch.

2. Strong understanding of dataset quality considerations for Computer Vision Applications.

3. In-depth knowledge of Model Training Dynamics, including the ability to identify and address errors based on training/evaluation metrics.

4. Sound theoretical and practical grasp of Deep Learning fundamentals, such as Convolutional Neural Networks (CNNs), Regularization Techniques, etc.

5. Familiarity with cutting-edge models like YOLO-series, Efficient Net/Efficient Det, etc.

6. Experience in utilizing Docker containers for Computer Vision and Deep Learning tasks.

Good to Have: 1. Experience in employing Model Optimization techniques like Pruning. 2. Familiarity with FP16/Mixed Precision and INT8 Optimized Training methodologies. 3. Experience: 1 to 2 years