Python + GenAI interview questions asked in Bangalore in 2026

What companies actually test

Hiring for Python + GenAI in Bangalore is no longer a Python syntax screen plus a chatbot demo. Interviewers want you to design a retrieval pipeline, choose a chunking strategy, explain evaluation, and talk about agents without hallucinating architecture.

At MufaSoft we run mocks against that bar: Python core, Django or Flask service design, RAG, LLM evaluation, and a short Agentic AI design (LangGraph or CrewAI). We do not sit the interview for you.

Python and backend rounds

Expect list/dict comprehension, generators, GIL, asyncio vs threads, and SQL joins. Then they jump to APIs: pagination, auth, idempotency, and how you would wrap an LLM call behind a Django or Flask endpoint with timeouts and retries.

• Write a FastAPI/Flask route that streams an LLM response and handles client disconnects.
• Where do you store embeddings vs metadata, and why?
• How do you keep secrets out of prompts and logs?

RAG, LLM and Agentic AI rounds

Interviewers ask you to sketch a RAG system for an internal knowledge base: chunk size, overlap, hybrid search, rerank, citations, and what you measure (faithfulness, context recall, latency). For agents they want a graph: tools, state, human-in-the-loop, and how you stop loops.

• Naive RAG vs agentic RAG — when is an agent worth the cost?
• How do you evaluate a RAG change without waiting for production tickets?
• LangGraph vs CrewAI vs a plain tool-calling loop — trade-offs.

How we prep you

You get role-wise question banks, a recorded mock with a mentor who ships this stack, and a scorecard on retrieval design, Python, and communication. Working professionals usually need 6–10 mocks; freshers need more Python + SQL first.