How to Get a QA Job in 2025 with ChatGPT in the Room

Yuliia Baranetska — Fri, 27 Jun 2025 16:35:17 GMT

💡 Spoiler: “Selenium + Postman” isn’t enough anymore. You’ll need to speak fluent Prompt, tame flaky tests with Visual AI, and know where LLMs fail. But don’t worry, you’ve got this.

2025: Same Interview Stress, Smarter Questions

When I started out, job ads asked for “SQL knowledge” and “attention to detail.”

Now? “Experience with AI-powered QA platforms” and “ability to generate prompt-driven regression suites.”

Even interviews have changed. You’re no longer asked “What’s a bug?” but…

💬 “How do you validate a ChatGPT test output and embed AI triage into your CI?”

Also, yes. AI filters your resume now. And candidates? They train with AI interviewers.

What They Ask Now and What It Means

Trend	What It Means
Self-healing tests	Tests that adapt automatically when UI changes, like updating selectors on the fly.
LLM (Large Language Model)	Models like ChatGPT that can generate test cases, prompts, and code reviews.
Visual AI vs flaky tests	Uses visual snapshots instead of DOM locators to reduce test instability.
AI Triage	Automated bug classification and prioritization, often plugged into CI/CD.
Prompt Engineering	The skill of crafting precise prompts for better LLM output.
testRigor / Katalon	AI-first test frameworks that reduce code and speed up automation via natural language.

QA Classics vs AI-Era Expectations

The Old-School Stack	The 2025 Reality
Agile / Kanban	Where does AI plug into your SDLC and why?
SQL / Postman	Write a prompt for boundary tests on `/orders` API
Selenium locators	Use Visual AI to stabilize flaky tests
Types of testing	AI defect prediction vs. traditional risk-based testing
“QA vs QC?”	Where do LLMs help, and where do they fail?

Let’s break this down. Here’s how I’d prep if I had just 7 days:

7-Day Interview Prep Plan

Day 1: Open 10 job listings. Highlight every AI/GenAI reference

Day 2–3: Try testRigor or Katalon. Screenshot a self-healing test in action

Day 4: Write 5 prompts (for testing, logs, APIs, code review, bug triage)

Day 5: Run a mock interview with ChatGPT Voice. Ask it 20 AI QA questions

Day 6: Record a Loom demo showing how AI cut regression time by 30%

Day 7: Write 3 STAR stories: one for self-healing, one for Visual AI, one for triage

What You Can Do During the Interview

- Generate a test case in ChatGPT and explain how you'd validate the result ( it do works! )

- Take a log file and build a prompt to highlight anomalies or missed assertions

- Ask the company: “What part of your pipeline do you trust AI with, and why?”

Your Resume, AI-Ready

- Tailor it with 10–15 keywords from each job post → feed into LLM → human edit

- Add a “Tooling” section: Prompt Engineering, Self-Healing, AI Bug Triage

- Include a GitHub repo: test prompts, flaky test reductions, CI output samples

Common Mistakes I’ve Seen

“AI is magic” - it’s not. If you can’t explain false positives, don’t deploy
Tried Katalon once, got excited, and stopped there. Don’t.

Try at least 2–3 tools and you’ll learn more from comparing them than from the docs.
“LinkedIn says you use AI, but you can’t show a diff”

Before you go in, here’s what I keep in my own interview prep list:

Interview Checklist

Examples where you used AI (with results)
A prompt + explanation of how you validated the output
A good question: “Which part of your CI/CD pipeline relies on AI and how much?”

Final Thoughts

Being a QA engineer in 2025 doesn’t just mean “you test stuff.” You’re now part reviewer, part AI whisperer.

I’ve seen candidates talk confidently about AI and then fall flat on a basic prompt. Don’t be that person.

*That’s how you avoid staring at a PR and whispering ‘what the hell is this?’ at 2AM.*r.

AQA Lab