Info Edge (Naukri)

Typical interview loop

How Info Edge (Naukri) usually runs it for applied AI and LLM engineer roles.

1. 1

   Online assessment

   Focus: DSA and CS basics

   Campus and early-career engineering hires report an online test mixing timed coding problems with multiple-choice CS questions before any interviews.

2. 2

   Technical rounds

   Focus: Math, classical ML, deep learning

   Data science candidates go through several technical interviews that probe statistics, linear algebra, classical ML and deep learning.

3. 3

   Live programming case study

   Focus: Applied problem solving in code

   You work through a realistic business problem in code, live, with an interviewer watching how you approach it.

4. 4

   Senior data science leader round

   Focus: Depth and DS judgment

   A senior leader from the data science group checks technical depth and how you think about data science problems.

5. 5

   HR round

   Focus: Culture fit

   A closing conversation on personality, motivation and culture fit.

Topics to prepare for Info Edge (Naukri)

The topics its loop weighs most, each with what interviewers check and what to study.

• Embeddings basics: word2vec, contrastive learning, what embeddings captureML fundamentals
• Python for ML interviewsCoding
• Calling LLM APIs in Python: async, streaming, retriesCoding
• Self-attention from scratchTransformers
• The decoder-only transformer (GPT forward pass)Transformers
• Retrieval for RAG: chunking, hybrid search, rerankingRAG
• Evals and error analysisEvals
• Decoding: greedy, beam, temperature, top-k/top-pInference

How it weights your plan

Selecting Info Edge (Naukri) when you build a plan multiplies each topic’s priority score: