Infosys AI Engineer interviews, as candidates describe them, center on your own GenAI projects, especially RAG architecture, with Python, deployment and LLM-concept questions. Reports range from a single round to multi-stage processes.
$113K
Avg. Base Comp
$140K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Infosys AI Engineer interviews, as candidates describe them, revolve around your own GenAI project work, especially RAG. Several people were asked to walk through a project's architecture and justify each design choice rather than just describe what they built. One candidate was asked about RAG versus fine-tuning, reranking in RAG, what retrieval means in LangChain and why they chose a specific model. Another was asked to design a RAG workflow covering document ingestion, chunking, embeddings and retrieval.
The structure varies. One candidate had a single 45-minute technical round and another a 15-20 minute Teams conversation. Others went through two rounds: a technical round followed by a managerial one, or two technical rounds of about an hour each. One candidate who received an offer described an aptitude test, a technical test on LLMs, RAG and generative AI, a technical interview and a closing HR round. Coding load differs too. One candidate was asked to implement a custom self-attention mechanism from scratch. Others describe rounds that were project-driven rather than algorithm-heavy.
Other reported topics include:
Azure OpenAI and Copilot Studio appeared in one report. Outcomes are mixed, with offers, rejections and one candidate still waiting. Be ready to defend every project on your resume with specific tradeoffs.
Synthesized from 10 candidate reports 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 | |
| Size of Joins | |
| Top 3 Users | |
| Covariance vs Correlation | |
| Find the Missing Number | |
| Retailer Data Warehouse | |
| Bagging vs Boosting | |
| Causal Attention Debugging | |
| Booking Regression | |
| Hurdles In Data Projects | |
| Fine-Tuning VS RAG | |
| P-value to a Layman | |
| Digitizing Student Test Scores | |
| Normalize Grades | |
| RAG Strict Source Control | |
| Concurrent LLM Serving | |
| Safe Streaming Latency | |
| Ticket Agent Analysis | |
| Find Duplicate Numbers in a List | |
| RAG Hallucinations | |
| String Palindromes | |
| Cloud-Agnostic Deployments | |
| 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.
Candidates describe entry through resume shortlisting or a contact after applying online. One candidate took an aptitude test and then a technical test on LLMs, RAG and generative AI before the interviews. Another mentioned a quick aptitude component testing basic reasoning.
The technical conversation is typically built around resume projects, with follow-ups on architecture and decisions. Reported topics include RAG versus fine-tuning, reranking, chunking, hallucination handling, agentic versus generative AI and Python fundamentals. One candidate also faced Docker, FastAPI concurrency, Celery monitoring and database index questions.
Coding expectations differ by report. One candidate was asked to implement a custom self-attention mechanism from scratch and struggled with it. Others describe rounds that were project- and discussion-driven rather than algorithm-heavy, though coding fundamentals were still expected. Prepare for Python basics and for transformer mechanics.
One candidate reports a managerial round after the technical round. It covered overall project experience, problem-solving approach and communication skills, and it ran lengthy and detailed. Expect deeper questions on the architectural decisions behind your projects.
One AI Engineer candidate describes a fairly standard HR round at the end of the process. Outcomes across reports are mixed: accepted offers, a declined offer, rejections and one candidate still waiting for a reply. Two candidates mention compensation or salary expectations as part of how their process ended.
