ZS LLM Engineer Interview Experience

I was approached for the LLM Engineer role at ZS through a third-party agency that found my profile on Naukri.com. After expressing interest, I submitted the required deta...

I was approached for the LLM Engineer role at ZS through a third-party agency that found my profile on Naukri.com. After expressing interest, I submitted the required details for the application. Background Previous Experience: 14 months as an AI Engineer at a startup. The selection process involved three rounds:

Round 1 Resume Shortlisting My resume was shortlisted, and ZS’s HR reached out to schedule the next steps. They expressed interest in my profile, finding it a promising fit for the role.

Round 2 Technical Interview Duration: 1 hour This round began with a brief introduction and discussions on my previous work experience and projects. The questions covered a mix of technical and coding topics: Key Topics Covered RAG Application Development: Procedure to build a notebook-based RAG (Retrieval-Augmented Generation) application. Concepts like VectorBases and steps involved in RAG. Coding Challenges: Find the longest repeated substring that appears at least twice in a string (e.g., for s='banana' , the result is 'ana' ). Detect a loop/cycle in a linked list. LLM Benchmarking: Definitions of MMLU, HELM, HumanEval, and Big-Bench. Hyperparameters in LLMs: Concepts of Top-K, Top-P, Temperature, etc. Advanced Concepts: CoT (Chain of Thought) reasoning. Self-Attention model, Encoder, Decoder, and their roles. Generator and Discriminator used in GANs. Towards the end of the interview, I sought feedback on my performance and advice for improving my AI skills.

Round 3 Experience-Based Interview Duration: 30-45 minutes This round was conducted by a senior LLM engineer or a ZS partner. It was largely based on my resume and work experience. Key Questions Asked Introduction: Briefly introduce yourself. About ZS: What do you know about ZS? Why do you want to join ZS? Work Experience: Detailed discussion on the GenAI products I built, including their usage metrics. Personal Questions: Family background. AWS Experience: Questions about the AWS tools mentioned in my resume. Specific discussion on my experience with AWS Bedrock. Latest Advancements in GenAI: Insights on topics like Hybrid RAG, multimodal agents, and MoE LLAVA. Closing Questions At the end, I asked about: The team structure at ZS. New products they are developing. The interviewer’s experience of being part of ZS. Final Thoughts The interview process was highly engaging and covered a mix of technical depth and real-world applications of LLMs and GenAI. It was a great opportunity to reflect on my skills and learn about the innovative projects at ZS.