“Included as a consequential modern Indian agent infrastructure employer with relevant product, engineering, data or AI roles.”
4
stages
2–5 weeks
end to end
20
practice questions
01
The interview, stage by stage
- 1
Founder or hiring manager screen60 min
tool callingAPI integrationsPrepare one quantified example demonstrating tool calling.
- 2
Practical role exercise60 min
API integrationsagent reliabilityPrepare one quantified example demonstrating API integrations.
- 3
Technical/product deep dive60 min
agent reliabilitydeveloper experiencePrepare one quantified example demonstrating agent reliability.
- 4
Team values and ownership round90 min
developer experienceevaluationPrepare one quantified example demonstrating developer experience.
02
What decides the offer
Tool calling
25%
Shows specific decisions and measurable evidence for tool calling.
API integrations
20%
Shows specific decisions and measurable evidence for API integrations.
Agent reliability
20%
Shows specific decisions and measurable evidence for agent reliability.
Developer experience
20%
Shows specific decisions and measurable evidence for developer experience.
Evaluation
15%
Shows specific decisions and measurable evidence for evaluation.
They look hardest for tool calling, API integrations, agent reliability, developer experience.
03
Your four weeks
Days 1–2
Company and market
- ·Map products, users, revenue model and competitors
- ·Write a one-page company thesis
Days 3–5
Core role skills
- ·Practice tool calling
- ·Practice API integrations
- ·Practice agent reliability
Days 6–8
Company-context cases
- ·Design reliable tool execution across hundreds of APIs.
- ·Prevent duplicate or unsafe agent actions.
- ·Evaluate an integration layer end to end.
Days 9–11
Technical simulations
- ·Complete two timed exercises
- ·Run one architecture/product mock
- ·Practice follow-up pressure
Days 12–14
Stories and final loop
- ·Prepare six quantified ownership stories
- ·Rehearse project deep dive
- ·Prepare interviewer questions
04
Practice these
- Design a production RAG system for ten million documents.Recommended
- How would you defend an LLM application against prompt injection?Recommended
- Design an agent that can safely call external tools.Recommended
- Offline evaluations improved but production metrics fell. What happened?Recommended
- Design an ablation study for a new agent architecture.Recommended
- How would you design multi-tenant retrieval without leaking customer data?Recommended
- Design a batching service for model inference.Recommended
- Design an account-research and personalization pipeline for 10,000 prospects.Recommended
- Design an enterprise architecture for using multiple model providers.Recommended
- How do you investigate a production incident involving unsafe model output?Recommended
- Design a model router that balances quality, latency, and cost.Recommended
- Design an AI coding assistant for a large enterprise codebase.Recommended
- Design memory for a long-running personal AI assistant.Recommended
- How would you evaluate a multi-agent system?Recommended
- Design a text-to-video generation system.Recommended
- Design a production multimodal document understanding system from data collection through serving.Recommended
- How would you improve the quality, latency, and cost of a multimodal document understanding system without masking regressions?Recommended
- Design a production vision-language models system from data collection through serving.Recommended
- How would you improve the quality, latency, and cost of a vision-language models system without masking regressions?Recommended
- Design a production vector databases system from data collection through serving.Recommended
05
Where people slip
- !Generic enthusiasm about startup culture or AI
- !No understanding of the company business model
- !Ignoring Indian price sensitivity, regulation or operational variance
- !Architecture without failure recovery and observability
- !Claiming a recommended case was actually asked
06
Ask them this
- ?What is the exact loop for this team and level?
- ?Which rounds permit AI tools?
- ?What would I own in the first 90 days?
- ?What is the hardest product or model constraint the team faces in India?
- ?How does the team measure quality after launch?
- ?How are ESOPs valued and what is the exercise policy?
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