Prior experience developing deep learning models for computer vision applications. Hands on experience in areas like image classification, object detection and instance segmentation.
Understanding of deep learning fundamentals including network architectures, training artefacts, overfitting and regularisation in neural networks, batch normalisation and so on.
Proficiency in python.
Proficiency in numpy and opencv.
Proficient in at least one of the deep learning frameworks e.g. PyTorch, MxNet or Tensorfiow.
Familiarity contributing and maintaining code in github.
Understanding of SQL and docker technology is a bonus.
Responsibilities:
Contribute across different stages of deep learning model development including data collection, data cleaning, model development, validation and deployment.
Develop deep learning models for a wide variety of tasks spanning object detection, segmentation, tracking, synthesise and so on.
Continuously improve the quality and performance of existing deep learning models by applying latest research in the field.
Contribute in developing models in areas like self-supervised and semi-supervised learning
Educational Background
B.Tech/M.Tech (Optional), B.Sc./M.Sc., Computer Science, Maths, Statistics or equivalent field
