Delhivery
Logistics company whose data roles add probability and ML to coding tests
Interview in a few hours? A quick sprint for Delhivery
AI in interviews
No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.
Typical interview loop
How Delhivery usually runs it for applied AI and LLM engineer roles.
- Focus: DSA, probability, ML basics
Online assessment
A HackerEarth test of MCQs plus a few coding problems, with a probability, distributions and ML section aimed at data-science applicants.
- Focus: DSA, project understanding
Technical interview 1
Starts with your introduction and projects, then moves to live data-structures and algorithms problems.
- Focus: Database design, SQL, design
Technical interview 2
A deeper resume probe that can include designing a database and writing queries, with design questions for experienced hires.
- Focus: Motivation, fit
HR round
A standard conversation on motivation, strengths and weaknesses, teamwork and what you know about Delhivery.
Topics to prepare for Delhivery
The topics its loop weighs most, each with what interviewers check and what to study.
- Python for ML interviewsCoding
- Calling LLM APIs in Python: async, streaming, retriesCoding
- GenAI system designSystem design
- Embeddings basics: word2vec, contrastive learning, what embeddings captureML fundamentals
- Cost and latency engineering for LLM appsSystem design
- RAG vs fine-tuning vs prompting: choosing the approachSystem design
- Design an AI customer-support agentSystem design
- Design enterprise document search / a knowledge assistantSystem design
How it weights your plan
Selecting Delhivery when you build a plan multiplies each topic’s priority score:
Official prep and resources
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