1. 1 straight forward dsa based sliding window question on string
2. ml fundamentals,regularization, finetuning, deep learning
3. designing a production ready RAG chatbot for company ( just the hld of that (no coding required))

It went well i prepared for this in super detail hence could answer almost all of the questions.

I was asked to design how recommendation systems work on platforms like YouTube, Amazon, and other e-commerce platforms — basically, how recommendations on the first page are decided and what factors influence them. We also discussed A/B testing and the metrics used to evaluate recommendation systems, such as NDCG, click-through rates, testing strategies, and how to identify clickbait. There were also a lot of open-ended questions around the same topic.

I realised that I really needed to learn recommendation system design in depth. It was a relatively new area for me, although I did have some background knowledge, which helped me navigate the discussion and eventually clear the interview.

Initially, I was quite scared about how the interview had gone. The interviewer was very generous, and since most of the questions were open-ended, I wasn't sure whether my answers had actually met the expectations. But a few days later, I received a call from HR informing me that I had been selected!

Tips: Prepare ur ml resume like hell. Zeroth round was resume screening so make sure that ur resume is strong enough. Prepare transformer very ml and fundamentals should be very strong.