1. A fundamental understanding of ML and DL principles, algorithms, and neural network architectures is critical.
2. Proficient in Python and experienced with AI development tools like TensorFlow and PyTorch.
3. Familiarity with techniques like Generative Adversarial Networks and Variational Autoencoders, and specific applications in text (NLP) and image generation.
4. Expertise in text generation and working with Large Language Models (LLMs) is essential for many Gen AI applications.
5. Skills in data preprocessing, feature engineering, and understanding of MLOps (CI/CD pipelines, deployment) are key for practical implementation.