Infosys AI Engineer interview typically runs 4 rounds: aptitude test, technical test, technical interview, HR round. It usually takes a few rounds and is highly structured and project-driven.
$116K
Avg. Base Comp
$182K
Avg. Total Comp
4-5
Typical Rounds
2-4 weeks
Process Length
Our candidates report that Infosys is looking for more than familiarity with GenAI terms; they want to see whether you can defend the engineering choices behind a real system. In one experience, the conversation kept returning to the same pattern: explain the project architecture clearly, then justify why a specific model, retrieval setup, or deployment path made sense. Questions like RAG versus fine-tuning, reranking, and what retrieval means in LangChain suggest they care about whether you understand the mechanics well enough to make tradeoffs, not just repeat best practices.
A recurring theme is that the strongest signal comes from how concretely you can talk through your own work. Multiple candidates reported deep follow-ups on local LLM deployment, Docker behavior, FastAPI concurrency, database indexing, and monitoring failed Celery tasks, which tells us Infosys values people who can connect GenAI ideas to backend reliability. We also see client-facing and situational questions woven in, especially around how AI was actually used in the project and why certain decisions were made. The non-obvious make-or-break here is clarity under pressure: candidates who can walk an interviewer through the full system, including failure points and operational details, seem to stand out quickly.
Synthesized from 1 candidate report by our editorial team.
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Topics based on recent interview experiences.
Featured question at Infosys
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Top Three Salaries | |
| Merge Sorted Lists | |
| Top 3 Users | |
| Find the Missing Number | |
| Covariance vs Correlation | |
| Retailer Data Warehouse | |
| Hurdles In Data Projects | |
| Bagging vs Boosting | |
| Booking Regression | |
| P-value to a Layman | |
| Fine-Tuning VS RAG | |
| Normalize Grades | |
| Concurrent LLM Serving | |
| Ticket Agent Analysis | |
| Find Duplicate Numbers in a List | |
| String Palindromes | |
| Cloud-Agnostic Deployments | |
| Classification and Regression | |
| Swap Variables | |
| Pipeline Transformation Failures | |
| Check Matching Parentheses | |
| The Longest Journey | |
| Seller Type Modeling | |
| Safe Deployments | |
| Text Editor With OOP | |
| Azure Kubernetes Infrastructure | |
| Relational Migration | |
| Why Do You Want to Work With Us | |
| Rebalance Probabilities |
Synthesized from candidate reports. Individual experiences may vary.
Candidates are first shortlisted based on their resume. In this case, the process began after the applicant was selected from the initial application pool.
The first formal round was an aptitude test described as straightforward. It served as an initial screening before moving into the technical assessments.
This round focused on practical AI topics, especially LLMs, RAG, and generative AI. It appears to be a technical screening to check baseline knowledge before the interview.
This was the most important stage and went deep into the candidate’s GenAI projects. The interviewer asked for end-to-end explanations of project architecture, tradeoffs such as RAG versus fine-tuning, deployment details, backend concepts, and situational decision-making questions.
The last round was a standard HR interview. It likely covered general fit and closing discussions before the final offer decision.