Responsibilities

• Design, train, and optimise deep learning models for tasks such as NLP, computer vision, or generative AI.
• Develop high-performance training pipelines, including data pre-processing, augmentation, and batching.
• Conduct experiments, evaluate model performance, and iterate using metrics and validation frameworks.
• Deploy models into production systems using MLOps best practices.

Requirements

• Strong experience with deep learning frameworks (PyTorch, TensorFlow, JAX).
• Proficiency in Python and experience with scientific computing libraries (NumPy, Pandas).
• Experience training and optimising neural networks, including CNNs, RNNs, Transformers, or diffusion models.
• Hands-on experience with GPUs, CUDA, or distributed training.
• Familiarity with data pipelines and large-scale data processing.
• Understanding of model evaluation, hyperparameter tuning, and experiment track

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