Overall, the interview process consisted of around 5 to 6 rounds, with most of the interviews being focused on domain-specific knowledge and experience. The process typically included:

• Domain-focused rounds: The majority of the interviews assessed depth of knowledge, practical experience, and problem-solving within the relevant domain.
• DSA round: One round was dedicated to Data Structures and Algorithms, covering coding and general problem-solving skills.
• AI Breadth round: This round focused on a broad understanding of AI concepts, techniques, and applications, testing knowledge across different areas rather than deep expertise in a single topic.
• ML Design round: The final component focused on Machine Learning system/design problems, evaluating the ability to design ML solutions end-to-end, including considerations around data, modeling, evaluation, scalability, and deployment.

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