The interview process consisted of multiple rounds starting with an initial screening where my resume and basic understanding of machine learning concepts were discussed. This was followed by a technical round focusing on core ML topics such as supervised vs unsupervised learning, overfitting, evaluation metrics, and model selection. I was also asked to explain one of my projects in detail, including the architecture, challenges faced, and improvements made. There was a coding round with basic data structures and Python questions. Finally, there was a discussion on problem-solving approach and real-world ML applications.