EY

I recently attended an interview for an AI role at EY GDS. The interview was mainly focused on my technical fundamentals and the projects mentioned in my resume. The interviewer started with some basic HTML questions and then moved on to Machine Learning concepts such as supervised and unsupervised learning, classification vs regression, overfitting, model evaluation, and commonly used ML algorithms. The questions were mostly conceptual, but the interviewer also asked practical follow-up questions to check whether I understood how these concepts are actually used. The interview then shifted towards AI and Generative AI, where I was asked about AI, Deep Learning, LLMs, and Transformers. A major part of the discussion was around RAG (Retrieval-Augmented Generation). I was asked to explain what RAG is, why it is used, how embeddings and vector databases work, the complete RAG pipeline, and the difference between RAG and fine-tuning. I was also asked about hallucinations and how RAG can help reduce them. Some questions were connected to my projects, so I had to explain the technical choices I made and how my system worked. Overall, the interview was a mix of basic and practical technical questions. The interviewer seemed more interested in whether I genuinely understood the concepts rather than whether I could simply define them. My main takeaway is that for an AI role at EY GDS, it is important to have strong fundamentals in ML and AI, along with a clear understanding of modern GenAI concepts such as LLMs, embeddings, vector databases, and RAG. Also, anything mentioned in the resume should be prepared thoroughly because project-based questions can go quite deep.