Quantum Capital AI Labs Interview Experience – (Founding QA) | 7 YoE
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Quantum Capital AI Labs Interview Experience – QA/Automation Engineer | 7 YoE

Current Designation: Lead Automation QA Engineer

Round 1 – Exploratory Discussion

Duration: 30 Minutes

Panel:

  • Product Head (15+ Years of Experience)

Questions Asked

  1. Walk me through your professional experience and the domains you have worked in.
  2. Explain the key projects and automation initiatives you have led.
  3. How would you approach testing an internal ChatGPT-like product?
  4. What test scenarios would you consider for an AI-powered conversational application?

Discussion Points

  • Previous project experience and domain expertise
  • Automation ownership and responsibilities
  • Testing strategies for AI-powered products
  • Functional and non-functional testing considerations for conversational AI systems

Experience

The discussion was conversational and focused on understanding my background, testing experience, and approach to quality engineering. A significant portion of the discussion revolved around testing AI-powered applications and how to validate the quality, reliability, and usability of such systems.

The interviewer was approachable and encouraged detailed discussions around practical testing strategies and real-world scenarios.

Verdict

✅ Cleared

Round 2 – Technical Testing Round

Duration: Approximately 1 Hour

Panel:

  • AI Engineer (10+ Years of Experience)

Questions Asked

Testing Fundamentals

  1. Explain SDLC and STLC.
  2. Different testing artifacts and templates used during the testing lifecycle.
  3. Test planning and execution approaches.
  4. Scenario-based testing questions.
  5. Manual testing concepts and best practices.

AI / LLM Testing

  1. How would you test a RAG (Retrieval-Augmented Generation) system?
  2. What are the key validation points for LLM-based applications?
  3. Challenges involved in testing AI-powered systems.
  4. Methods to evaluate response quality and accuracy.

Automation

  1. Write a simple Selenium script for a login workflow.
  2. Follow-up questions related to the implementation.
  3. Discussion around automation best practices and framework concepts.

Experience

This round focused heavily on core testing fundamentals, manual testing concepts, and AI-specific testing approaches.

The interviewer explored both traditional QA knowledge and modern AI testing considerations. The Selenium coding exercise was straightforward and served as a starting point for additional discussions around automation practices and framework design.

The conversation was interactive, and the interviewer asked several follow-up questions to understand my thought process and practical experience.

Verdict

✅ Positive Feedback During Discussion

At the end of the interview, the interviewer mentioned that HR would reach out to schedule the next technical round.

Post Interview Experience

The recruitment process was being coordinated by a third-party recruiting agency.

After the second round, I did not receive any communication regarding the next steps for approximately one week. During this period, I attempted to contact the recruiter via phone but was unable to reach them.

I subsequently sent a follow-up email requesting an update on the interview process. In response, I was informed that the company had decided to move forward with another candidate and that the position was no longer open.

Final Outcome

❌ Position Closed / Another Candidate Selected

Overall Experience

Difficulty Level

Very Easy

Topics Covered

  • SDLC & STLC
  • Manual Testing Fundamentals
  • Test Planning & Documentation
  • Selenium Automation
  • AI/LLM Testing
  • RAG Testing Concepts
  • Conversational AI Product Testing
  • Scenario-Based Testing

What to Prepare

  • Strong understanding of testing fundamentals.
  • End-to-end testing approaches for AI-powered products.
  • RAG and LLM testing concepts.
  • Basic Selenium coding exercises.
  • Test strategy discussions for conversational AI applications.

Final Thoughts

The technical discussions were relevant and focused on both traditional QA practices and emerging AI testing concepts. The interviewers were professional, and the conversations were engaging throughout the process.

The only area that could have been improved was communication regarding the final status of the position, particularly since the interview process was being coordinated through a third-party recruiter.

Overall, it was a positive learning experience and provided good exposure to the expectations for QA roles working on AI-driven products.

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