Mastercard AI Engineer Interview Experience (SWE-1)
Current Ex:- 1Y 5M
Current company- Product Based
CTC offered 16L Base,2L Bonus
Location Pune
Recently interviewed for an AI Engineer role at Mastercard. Sharing my complete interview experience since it might be useful for anyone preparing for similar AI/GenAI engineering roles.
The overall process had an Online Assessment, 3 interview rounds, followed by an HR discussion.
Online Assessment — 60 minutes
The OA was conducted on HackerRank.
There were 2 coding questions, both around medium difficulty. The questions mainly tested DSA fundamentals, problem-solving, and the ability to implement a clean solution within the given time.
For DSA preparation, I had mainly been practicing from the Striver A2Z DSA Sheet and codestorywithMIK.
Round 1 — DSA + Projects + LLD | 60 minutes
This round was a mix of coding and technical discussion.
It started with 2 DSA questions. Apart from getting the solution, the interviewer was interested in the approach, complexity, and possible optimizations.
After DSA, the discussion moved to my previous projects. I was asked to explain the architecture, my contribution, some of the technical decisions I had made, and challenges I faced.
There were also questions around:
- LangChain basics
- OOP concepts
- LLD fundamentals
- Design patterns and when to use them
Overall, this round felt more like a general software engineering round with some AI/GenAI context.
Round 2 — GenAI Deep Dive | 60 minutes
This was the most GenAI-focused round of the entire process.
We started with my previous AI/GenAI projects. The interviewer asked several follow-up questions about how the systems worked internally rather than just discussing what I had built.
A major part of the interview was focused on RAG and MCP. Roughly 30 minutes were spent discussing these two topics in depth.
For RAG, the discussion covered the complete pipeline — document ingestion, chunking, embeddings, vector databases, retrieval, context construction, generation, and evaluation. There were also questions around practical choices such as chunking strategies, retrieval quality, and improving the overall pipeline.
For MCP (Model Context Protocol), the questions went into concepts such as MCP Host, Client and Server, tools/resources, communication between components, and how MCP can be used when building agentic applications.
There were additional questions around:
- LangChain
- Google ADK
- AI agents
- Tool calling
- Agent workflows
- RAG architecture
- MCP architecture
For RAG and MCP, I had prepared from multiple notes and documentation. Two structured resources I used during revision were:
MCP: https://www.targettrek.in/book/genai/mcp
RAG: https://www.targettrek.in/book/genai/rag
This round was quite detailed, but I was able to answer around 90–95% of the questions.
Round 3 — Managerial + Technical | 60 minutes
The managerial round was not purely behavioral. It had a good amount of technical discussion as well.
We discussed my projects again, but this time the questions were more focused on decision-making and trade-offs — why I chose a particular approach, what alternatives I considered, and what I would change if I had to redesign the system.
There were behavioral questions around teamwork, handling disagreements, challenges, ownership, and situations where things didn't go as planned.
I was also asked:
- Why Mastercard?
- RAG concepts
- MCP concepts
- Fine-tuning
- LLD fundamentals
- HLD fundamentals
- General system-design theory
So I would recommend not treating the managerial round as a non-technical round, especially for an AI Engineer role.
HLD: https://www.targettrek.in/book/system-design/hld
HR Round
The final discussion was with HR and was mostly around:
- Current role and experience
- Expectations
- Compensation
- Salary discussion
- General joining-related discussion
Other Offers - https://leetcode.com/discuss/post/8540720/mastercard-amazon-and-salesforce-sde1-ne-xfs7/
Overall Experience
The interview process had a good balance of DSA, software engineering fundamentals, system design, projects, and GenAI.
One thing I noticed was that simply knowing how to use LangChain or an AI framework wasn't enough. The interviewers were interested in whether I understood what was happening underneath — especially for RAG and MCP.
If you're preparing for similar AI Engineer/GenAI roles, I would focus on:
DSA + Projects + LLD/HLD fundamentals + RAG + MCP + AI agents + LangChain + one agent framework such as Google ADK.
Also, be prepared to explain your own projects very deeply. Almost every round had questions based on previous project experience.
Hope this helps anyone preparing for an AI Engineer interview at Mastercard or similar GenAI roles. All the best! 🚀
