Krutrim

All AI roles · updated 2026.07

“Included as a consequential modern Indian foundation ai employer with relevant product, engineering, data or AI roles.”

stages

2–5 weeks

end to end

practice questions

Hiring now

AI engineer roles are open right now

landed applies to roles like these for you, on each company’s own site, with answers written for the role.

Get 5x interviews10 free auto-applies. No card.

AI Engineer
InMobi · San Mateo, CA
Staff AI Engineer
Coupang · Seattle, USA
AI/ML Engineer
Kaizen Labs · New York, NY

Or practice this loop first

The interview, stage by stage

1
Founder or hiring manager screen60 min
transformers and post-trainingIndic languages
Prepare one quantified example demonstrating transformers and post-training.
2
Practical role exercise60 min
Indic languagesdistributed training
Prepare one quantified example demonstrating Indic languages.
3
Technical/product deep dive60 min
distributed trainingevaluation
Prepare one quantified example demonstrating distributed training.
4
Team values and ownership round90 min
evaluationresearch taste
Prepare one quantified example demonstrating evaluation.

What decides the offer

Transformers and post training

25%

Shows specific decisions and measurable evidence for transformers and post-training.

Indic languages

20%

Shows specific decisions and measurable evidence for Indic languages.

Distributed training

20%

Shows specific decisions and measurable evidence for distributed training.

Evaluation

20%

Shows specific decisions and measurable evidence for evaluation.

Research taste

15%

Shows specific decisions and measurable evidence for research taste.

They look hardest for transformers and post-training, Indic languages, distributed training, evaluation.

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 transformers and post-training
·Practice Indic languages
·Practice distributed training

Days 6–8

Company-context cases

·Design an evaluation suite for Indic-language LLMs.
·Train speech or text models with code-mixed data.
·Choose between model scaling and data-quality improvements.

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

Practice these

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

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?