New opportunity

AI QE Engineer - Associate

PwC Acceleration Centres · Bangalore, India

About this role

The AI QE Engineer - Associate supports client projects in PwC Acceleration Center India’s Technology Consulting practice. The role focuses on applying quality engineering standards to software development and testing, including the use of AI tools to improve testing workflows.

Responsibilities

Design and manage quality assurance processes and metrics; generate and execute test cases and protocols.
Conduct functional, compatibility and performance testing; identify and resolve defects to improve reliability, performance and user experience. Use Selenium for browser automation and participate in systems integration and user acceptance testing.
Collaborate with cross-functional teams to validate integrated systems, provide post-go-live support and improve testing processes.

Requirements and additional capabilities:

A bachelor’s degree or higher and oral and written proficiency in English are required. No overall years-of-experience requirement is stated.
The posting identifies hands-on use of at least two of GitHub Copilot, ChatGPT, Cursor, Claude and Gemini as a must for testing tasks such as requirements analysis, scenario identification, test-case and test-data generation, automation code generation, execution support and defect analysis.
Additional capabilities described include creating and refining prompts; understanding LLMs, context windows, hallucinations, temperature, grounding and RAG; and critically reviewing AI-generated tests and code for correctness, coverage, security and maintainability.
Relevant automation technologies include Playwright with MCP, Selenium with MCP, Tosca, Karate and REST Assured. The posting also mentions MCP server connectivity, TypeScript or JavaScript, Java or Python, and automated test execution through Jenkins, GitHub Actions or Azure DevOps. Exposure to testing chatbots, copilots and generative AI applications includes assessing response quality, hallucinations, groundedness and safety. AWS, Azure or GCP and test-management tools such as Jira, Xray and Zephyr are also listed.
Good-to-have areas include Agile/Scrum, shift-left and continuous testing, JMeter, and experience in banking and insurance, pharma and healthcare, telecom, media or technology. Mathematics and Management Information Systems are also mentioned.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Quality assurance engineeringFunctional testingCompatibility testingPerformance testingTest automationSeleniumSystems integration testingUser acceptance testingGenerative AI testingTest-case generationTest-data generationDefect analysisPrompt engineeringLLMsRAGMCPPlaywrightToscaKarateREST AssuredTypeScriptJavaScriptJavaPythonJenkinsGitHub ActionsAzure DevOpsAWSAzureGCP

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Build measurable QA coverage
The role designs QA processes and metrics, generates and executes test cases, and conducts functional, compatibility and performance testing. Prepare to explain how requirements become test coverage and how results guide defect prioritization.
Use AI tools without surrendering judgment
The posting calls for hands-on use of at least two named GenAI tools for testing tasks and emphasizes reviewing generated tests and code for correctness, coverage, security and maintainability. Prepare an example of refining prompts and checking AI output against requirements.
Deliver practical test automation
Selenium browser automation is an explicit responsibility. The posting also highlights AI-assisted automation, code review and debugging, and execution through CI tools. Prepare to walk through an automation workflow, including how you diagnose failures and keep tests maintainable.
Validate integrated systems and support releases
The role involves cross-functional system validation, systems integration and user acceptance testing, defect resolution, and post-go-live support. Prepare to describe how you communicate a reproducible issue and verify a fix across affected workflows.

Likely challenges to think through

AI speed versus dependable tests
Preparation hypothesis: AI-generated scenarios and code may accelerate testing while introducing missing coverage or incorrect assumptions. Practice tracing generated cases to requirements, checking test data and reviewing code for security and maintainability before use.
Breadth versus testing depth
Preparation hypothesis: balancing functional, compatibility and performance testing could make it difficult to investigate every risk equally. Practice explaining how you would prioritize coverage using user impact, defect evidence and available time, without treating every test type as interchangeable.
Local fixes versus end-to-end reliability
Preparation hypothesis: a defect may appear resolved in one component but persist in an integrated workflow or after go-live. Prepare a concise approach to reproduction, cross-team communication and regression verification, including what evidence you would record.

Practice your answers

Technical practice

How would you turn software requirements into a test plan and measurable quality checks?

What to coverSuggested preparation: Explain how you would identify functional scenarios, generate and execute test cases, and track coverage and defects. Discuss how the metrics would help the team improve reliability rather than simply count tests.

How would you use two of the listed AI tools to develop test scenarios or test data?

What to coverSuggested preparation: Describe the requirements and context you would provide, how you would refine prompts, and what you would verify before using the output. Address gaps, unsuitable test data and the risk of invented requirements.

How would you review AI-generated tests or automation code?

What to coverSuggested preparation: Check expected results against requirements, then assess coverage, correctness, security concerns and maintainability. Explain how you would debug or revise output rather than accept it because it was generated.

How would you design a reliable Selenium browser test?

What to coverSuggested preparation: Cover meaningful assertions, stable element selection, waits and test-data control. Explain how you would investigate a failure before deciding whether it indicates a product defect or a fragile test.

How would you approach compatibility and performance testing?

What to coverSuggested preparation: Describe how you would select relevant environments and user workflows for compatibility checks. For performance, explain what you would measure, how you would establish a comparison point and how you would report a user-impacting issue.

How would you test an integrated system and support user acceptance testing?

What to coverSuggested preparation: Identify interface boundaries, data flow and end-to-end scenarios that require cross-functional validation. Distinguish technical integration checks from confirming that a workflow meets user needs.

What would you check when using MCP-connected tools to assist test automation?

What to coverSuggested preparation: Explain how you would confirm the connection, available context and tool output before relying on generated automation. Discuss reviewing and debugging code in a language named in the posting, such as TypeScript, Java or Python.

How would you run automated tests in a CI pipeline?

What to coverSuggested preparation: Describe where tests would run, how results would be reported and how you would investigate failures in a system such as Jenkins, GitHub Actions or Azure DevOps. Discuss the trade-off between fast feedback and broader test coverage.

How would you evaluate a chatbot or RAG-based application?

What to coverSuggested preparation: Consider response quality, groundedness, hallucinations and safety across representative prompts. Explain how you would compare an answer with its supplied context and document a failure that another tester could reproduce.

How would you diagnose and prioritize a defect found after go-live?

What to coverSuggested preparation: Describe how you would reproduce the issue, gather evidence and assess its effect on functionality, performance or user experience. Explain how you would communicate findings and verify the fix while supporting affected users.

Behavioural practice

Tell me about a time you worked with other teams to validate a software change.

What to coverSuggested preparation: Use a real situation, your actions and the result. Focus on how you clarified responsibilities, shared test findings and resolved differing views of expected behavior.

Describe a time you adapted your testing approach to changing needs.

What to coverSuggested preparation: Give the situation, what changed, the actions you took to reassess priorities and the result. Keep the example grounded in how you maintained quality while responding to new information.

Tell me about a quality-assurance process you helped improve.

What to coverSuggested preparation: Explain the original problem, the specific change you made and the observed result. Be ready to discuss the evidence or feedback you used to judge whether the change helped.

Describe a time you supported others through a software issue after release.

What to coverSuggested preparation: Set out the situation, your actions to communicate and investigate, and the result for users or the team. Distinguish what you did from decisions made by others.

Tell me about a time you questioned an apparently plausible technical result.

What to coverSuggested preparation: Use a real example with the situation, how you checked the evidence and the outcome. If relevant, discuss how you handled an AI-generated suggestion or test result that needed correction.

Sharpen your knowledge

Topics to prepare

Quality engineering strategy and metrics
Map a sample requirement to a QA process, test protocol and two measurable quality indicators. The role calls for quality standards, QA processes and metrics to improve reliability.
Test levels and coverage
For a hypothetical integrated feature, distinguish functional, compatibility and performance checks from systems integration testing and user acceptance testing. All are named in the posting.
LLM behavior and grounding
Explain how context windows, temperature, hallucinations and grounding could affect AI-generated test cases; then outline how RAG could supply relevant source context. These concepts appear under “What Sets You Apart.”
Evaluating generative AI outputs
Draft criteria for judging a hypothetical chatbot response for quality, groundedness and safety, and for checking AI-generated tests for coverage and correctness. The posting explicitly mentions both kinds of review.

Skills to revise

AI-assisted test design with two tools
Practice using two of GitHub Copilot, ChatGPT, Cursor, Claude or Gemini to turn a sample requirement into scenarios, test cases and test data; refine the prompts and verify the results against the requirement. The posting calls hands-on use of two tools a must in its “What Sets You Apart” section.
Selenium browser automation
Write and run a small Selenium test for a hypothetical browser workflow, including assertions and a failure report. Selenium browser automation is an explicit responsibility.
Functional, compatibility and performance execution
Create a compact test matrix for a sample feature, execute the checks in a practice environment and record reproducible defects. The role requires these test types and defect identification.
Reviewing AI-generated automation code
Generate a small test in one listed language—TypeScript, JavaScript, Java or Python—then debug it and check assertions, security concerns and maintainability. The posting stresses critical review rather than accepting generated code blindly.
Automated test execution and defect triage
As a preparation exercise, run a sample test suite through one listed option—Jenkins, GitHub Actions or Azure DevOps—and summarize a failed run with evidence for triage. The posting mentions automated-test integration and execution, defect analysis and post-go-live support; it does not prescribe a specific pipeline.

Your preparation checklist

Build a test-plan example
Prepare a sample requirements-to-test matrix with functional, compatibility and performance cases, test data, execution results and two quality metrics. These reflect responsibilities stated in the posting.
Walk through browser automation
Prepare a Selenium test walkthrough showing the scenario, code, assertions, failure investigation and a maintainability improvement. If applicable, show how it runs in a CI pipeline; the posting names Jenkins, GitHub Actions and Azure DevOps.
Show how you review AI-assisted testing work
If you have used two of GitHub Copilot, ChatGPT, Cursor, Claude or Gemini, prepare a concrete example from each: the prompt, generated test or code, and corrections you made for coverage, correctness, security or maintainability. The posting calls hands-on use of two tools a must.
Practice a generative-AI evaluation exercise
As preparation for the listed chatbot and GenAI testing capability, draft a small set of prompts, expected evidence and pass/fail criteria for response quality, groundedness, hallucinations and safety. Label this as a practice exercise unless it reflects work you have done.
Prepare a defect-to-resolution story
Choose a real example, if available, that shows defect reproduction, severity reasoning, collaboration on a fix and retesting. Be ready to explain how you would adapt the approach for systems integration, user acceptance or post-go-live support, all mentioned in the role.
Gather evidence for stated requirements
Have a concise account of your degree and examples of clear written and spoken technical communication. The posting explicitly requires at least a bachelor’s degree and oral and written English proficiency; it states no overall years-of-experience threshold.
Write questions about the testing environment
Bring questions about which automation stack and AI tools the team uses, how AI-generated tests are reviewed, what quality metrics matter on client projects, and how integration testing and post-go-live support are shared across teams.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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