Infosys AI Engineer candidates report interviews centered on defending GenAI project choices, RAG and LLM concepts, practical deployment details, and occasional lightweight live coding.
$96K
Avg. Base Comp
$102K
Avg. Total Comp
4 rounds
Typical Rounds
2-4 weeks
Process Length
Infosys AI Engineer interviews in these reports are notably project-driven rather than algorithm-heavy. Be prepared to walk through a GenAI project from architecture to deployment: why you chose RAG or fine-tuning, how retrieval and reranking work, the model selection rationale, and the production tradeoffs behind your decisions. Candidates were pressed to explain implementation choices on their own resumes, including Python packages, vector indexing and chunking, local LLM deployment, Docker configuration, FastAPI concurrency, database indexes, and monitoring failed Celery tasks.
Core LLM concepts also matter in context. Reported questions covered temperature, reducing hallucinations, guardrails, and handling token latency under concurrency. One candidate encountered a live self-attention implementation, while another received a simple Python string-reversal exercise; that variation makes it sensible to refresh coding fundamentals without treating them as the central focus.
The format varies substantially across reports: two describe a single technical conversation, one of them virtual, while another describes aptitude and technical tests before technical and HR discussions. Thin evidence means the exact sequence may differ by team. Focus your preparation on explaining real work clearly, including tradeoffs and failures, rather than memorizing high-level GenAI terminology.
Synthesized from 3 candidate reports by our editorial team.
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Real interview reports from people who went through the Infosys process.
The single-round technical interview lasted 45 minutes and focused heavily on core GenAI engineering principles and problem-solving agility. It began with a brief 5-minute resume walkthrough, where the interviewer asked me to explain the architecture of a Retrieval-Augmented Generation (RAG) pipeline I previously built. I felt highly confident explaining our vector database indexing strategy and chunking mechanisms. However, I struggled when tasked with a live coding challenge to implement a custom self-attention mechanism from scratch. What surprised me most was the interviewer’s deep focus on production bottlenecks, specifically how to mitigate token latency and handle hallucination guardrails under high concurrency.
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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 | |
| Size of Joins | |
| Top 3 Users | |
| Covariance vs Correlation | |
| Find the Missing Number | |
| Retailer Data Warehouse | |
| Bagging vs Boosting | |
| Booking Regression | |
| Hurdles In Data Projects | |
| Causal Attention Debugging | |
| Fine-Tuning VS RAG | |
| P-value to a Layman | |
| Normalize Grades | |
| Digitizing Student Test Scores | |
| RAG Strict Source Control | |
| Concurrent LLM Serving | |
| Safe Streaming Latency | |
| Ticket Agent Analysis | |
| Find Duplicate Numbers in a List | |
| RAG Hallucinations | |
| Cloud-Agnostic Deployments | |
| String Palindromes | |
| Classification and Regression | |
| Swap Variables | |
| Pipeline Transformation Failures | |
| Check Matching Parentheses | |
| The Longest Journey | |
| Seller Type Modeling |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reported an aptitude test followed by a technical test on LLMs, RAG, and generative AI before interviews. Other candidates described a single technical conversation instead, so an assessment stage may depend on the hiring path.
Candidates report detailed discussion of resume projects and work history, with follow-ups on how and why choices were made. Topics included RAG versus fine-tuning, retrieval and reranking, model choice, vector indexing, chunking, Python packages, and local LLM deployment.
Reported technical questions included temperature, hallucination reduction, LLM guardrails, token latency under high concurrency, FastAPI concurrency versus parallelism, Docker commands, database indexes, and monitoring failed Celery tasks. Expect questions to be tied to practical tradeoffs.
Live coding varied across reports: one candidate was asked to implement custom self-attention, while another received a Python string-reversal exercise. In one multi-stage account, a standard HR round followed the technical interview; candidates should treat both elements as possible rather than universal.
