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Overall, the interview process consisted of around 5–6 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.

Two interview rounds, coding round had one leetcode mediums and one problem on von neumann's trick second round was a resume/behavioural round, where they asked me about projects mentioned on resume.

Decent process. Well related questions based on the profile. We can easily clear if we are strong in fundamentals. Don't panic prepare well. You can definitely achieve it.all the best

Initially an online assessment was held which comprised of 2 DSA Questiond of medium-hard difficulty. After this two rounds of interview were held on campus - Technical Round and HM Round. The technical round comprised of DSA questions only with medium difficulty. The HM Round included basic HR Questions only

It was pretty easy.. they just asked one hashmap question and one to reverse a string. overall the interviewer were nice and okay to talk to and anyone can get past round 1...