Accenture Final Interview : Experience for LLM Operations Engineer (Experienced) – Selected

Last Updated : 26 Dec, 2025

Candidate Information:

  • Company: Accenture
  • Role: LLM Operations Engineer
  • Experience: 2+ years
  • Mode: In-location (Magarpatta, Pune) Virtual Interview (On-site, Interview conducted virtually)
  • Verdict: Selected
  • Date: 18/12/2025

Overview of Interview Process:

Initial Screening

The interview process consisted of two rounds:

  • Skill / Technical Round
  • Final Technical + Managerial Round (On-site Virtual)

I was informed in advance that the final round would be conducted from the Accenture office location. I was asked to carry a PAN card for identity verification.

At the office, Accenture provided me with a laptop and a dedicated setup, and the interview was conducted virtually with the panel.

Technical Round

Here is the complete list of questions which were asked to me. This order may not be chronological but it covers all the questions:

  • Tell me about yourself
  • Tell me about the latest project you worked on – I worked on RAG-based Test Case Generator; so spoke about that
  • What problem were you trying to solve with this project?
  • What are you storing in the Vector Database?
  • What tech stack did you use in this project?
  • Is your system single-agent or multi-agent?
  • What was your role, task, and responsibility in the project?
  • How do you ensure the quality of the output in your system?
  • Which framework did you use (LangChain or CrewAI) and why?
  • What are the components of CrewAI?
  • Can agents work asynchronously in CrewAI?
  • Can multiple agents work in parallel on different tasks?
  • What is the key difference between a Generative AI system and an Agentic AI system?
  • What do you know about Transformer architecture?
  • Explain step by step how RAG works behind the scenes
  • Why do we need RAG?
  • Have you heard about MCP (Model Context Protocol)?
  • What is __init__() in Python?
  • Why does Python use double underscores (__init__)?
  • Can you define a method with a single underscore prefix?
  • What is abstraction in Python OOP?
  • Can you write a simple example of inheritance in Python?
  • Why is routing required in a multi-agent system?
  • Have you used any CrewAI tools?
  • What tools have you used (SerperDevTool, EXA Search Tool)?
  • What does EXA Search Tool do?
  • How did you integrate EXA Search Tool into your project?
  • Can you write code to use EXA Search Tool?
  • Can you write a sample function from your RAG Test Case Generator to check output quality or similarity score?
  • What are REST API methods (GET, POST, PUT, PATCH, DELETE)?
  • What is the difference between PUT and PATCH?

I was asked if I had any questions. I asked:

  • How to prevent misuse of AI for blindly copying code
  • How AI should be used for learning new technologies
  • How to effectively use AI in daily engineering work

The discussion turned very interactive and insightful.

Post-Interview Reflections:

If you are preparing for roles like LLM Operations Engineer:

  • Build at least one end-to-end RAG or Agentic AI project
  • Understand how things work behind the scenes
  • Focus on clarity of thought, not buzzwords

Additional Information:

  • A few days later, I received confirmation along with onboarding documentation.

Closing Note:

  • Here is what I think where the interview was mainly focused upon. Here are more such pieces of wisdom you should consider before appearing for such roles:
  • Strong fundamentals in Python, OOP, and REST APIs are important
  • Real-world GenAI / RAG / Agentic AI projects matter a lot
  • Be clear about your role and ownership in projects
  • Think in terms of systems, reliability, and quality
  • Interviewers appreciate honest answers and curiosity - I was not able to write a code, I simply told him honestly. Trust me it works, you may not be perfect, but don't be dishonest while trying to be perfect.

Hope this helps. All the best! and happy coding

Comment

Explore