“Included as a consequential modern Indian data platform 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
data architecturegovernancePrepare one quantified example demonstrating data architecture.
- 2
Practical role exercise60 min
governancereliabilityPrepare one quantified example demonstrating governance.
- 3
Technical/product deep dive60 min
reliabilitydeveloper experiencePrepare one quantified example demonstrating reliability.
- 4
Team values and ownership round90 min
developer experienceenterprise adoptionPrepare one quantified example demonstrating developer experience.
02
What decides the offer
Data architecture
25%
Shows specific decisions and measurable evidence for data architecture.
Governance
20%
Shows specific decisions and measurable evidence for governance.
Reliability
20%
Shows specific decisions and measurable evidence for reliability.
Developer experience
20%
Shows specific decisions and measurable evidence for developer experience.
Enterprise adoption
15%
Shows specific decisions and measurable evidence for enterprise adoption.
They look hardest for data architecture, governance, 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 data architecture
- ·Practice governance
- ·Practice reliability
Days 6–8
Company-context cases
- ·Design a governed metadata and data-discovery platform.
- ·Handle lineage across fragmented enterprise systems.
- ·Add AI search without leaking restricted data.
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
- How would you design an AI assistant for doctors?Recommended
- How would you launch an AI feature whose output cannot always be objectively graded?Recommended
- 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
- Compare supervised fine-tuning, preference optimization, and reinforcement learning from feedback.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 reliable synchronization between a CRM, data warehouse, and enrichment providers.Recommended
- Design an enterprise architecture for using multiple model providers.Recommended
- How do you investigate a production incident involving unsafe model output?Recommended
- How should a team set release thresholds when safety metrics have uncertainty?Recommended
- How would you audit whether an AI system treats demographic groups fairly?Recommended
- Design a model router that balances quality, latency, and cost.Recommended
- How would you build a feedback loop without amplifying user bias or abuse?Recommended
- Design an AI coding assistant for a large enterprise codebase.Recommended
- Design memory for a long-running personal AI assistant.Recommended
- A model refuses too often after a safety update. How do you diagnose and fix it?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?
Free to read · better with Enzo
Get ready for this interview with Enzo
Enzo builds a prep plan for this company and runs mock rounds for each stage.