Interview Scenario Previews by Round Type
Coding0-2 years
Coding and Debugging
Competency: Python implementation and debugging under time pressure
What is tested: Can you read unfamiliar code, identify the bug, write a fix, and explain why the fix is correct?
Context: You are given a small Python pipeline component that processes data and calls an external API. The component has a bug that causes incorrect output under certain inputs.
Time expectation: 30-40 minutes
Answer framework: Read the code, identify the failure path, write a minimal test that reproduces the bug, fix the root cause, and verify the fix does not break other paths.
Safe follow-up: What if the API returns an unexpected response format — how would you handle that gracefully?
Applied2-5 years
Applied Build Round
Competency: RAG pipeline reasoning and retrieval quality
What is tested: Can you reason about ingestion, chunking, retrieval, reranking, and generation quality as a connected system?
Context: You are asked to design a RAG pipeline for a knowledge-base assistant. The interviewer asks what could go wrong at each stage and how you would detect it.
Time expectation: 40-50 minutes
Answer framework: Walk through each pipeline stage, state what could fail, how you would measure it, and what trade-off you would accept.
Safe follow-up: If retrieval recall is high but answer quality is low, where would you look first?
Applied2-5 years
Applied Build Round
Competency: Agent vs workflow choice and tool design
What is tested: Can you decide when an agent is justified, design tool contracts, and specify when human approval is needed?
Context: You are asked to build a customer-support assistant that can retrieve documents and call an API. The interviewer asks whether you would use an agent or a deterministic workflow.
Time expectation: 40-50 minutes
Answer framework: Clarify the task, compare agent vs workflow, state the failure modes of each, and choose based on reversibility and risk.
Safe follow-up: What if the tool returns an error — how should the agent behave?
Production5-8 years
Production Round
Competency: MLOps lifecycle and production operations
What is tested: Can you reason about reproducibility, promotion gates, drift detection, and rollback as one operational system?
Context: You are asked to explain how you would deploy, monitor, and roll back a trained model in production. The interviewer probes your understanding of drift, SLOs, and incident response.
Time expectation: 40-50 minutes
Answer framework: Map the lifecycle from data to retirement, identify gates, define monitoring signals, and describe the rollback procedure.
Safe follow-up: If you detect drift but the business cannot accept downtime, what do you do?
System Design5-8 years
System Design
Competency: End-to-end AI application system design
What is tested: Can you structure an end-to-end architecture from ambiguous requirements to a defensible design with recovery?
Context: You are asked to design an LLM-powered document analysis service that handles PDFs, retrieval, and citation-backed answers. The interviewer probes SLOs, failure modes, and cost.
Time expectation: 45-60 minutes
Answer framework: Clarify users and constraints, define SLOs, choose model and retrieval, design evaluation, plan failure recovery, and state capacity assumptions.
Safe follow-up: What if the corpus grows 10x — what changes in your architecture?