R
I've spent nineteen years in software quality engineering. I've tested telecommunications systems, retail point-of-sale platforms, healthcare billing applications, and distributed enterprise software that processes millions of transactions every day. I've seen the field change dramatically — from waterfall to agile, from manual testing to automation, from on-premise to cloud.
None of those transitions felt as disruptive as what's happening right now.
AI is not just changing how we test software. It's creating an entirely new problem that quality engineering hasn't had to solve before: who validates the AI that's doing the testing?
That question is what pushed me to start thinking differently about my own career — and about where quality engineering is heading as a discipline.
About a year ago, I was reviewing test results from an AI-assisted testing tool that a team I was supporting had integrated into their pipeline. The dashboard was clean. Coverage metrics looked good. The model was flagging anomalies, prioritizing regression tests, and producing outputs that looked exactly like what we'd asked for.
But something bothered me.
I started digging into the cases the model was not flagging. I found a pattern. The model was consistently confident about a category of transactions that had been relatively stable for two years — but the product had just added a new payment processing rule that introduced a subtle edge case. The AI tester had never seen anything like it in its training data, so it wasn't looking for it.
The binary metrics said everything was fine. The AI was succeeding at testing the wrong things.
That's when it clicked. We had spent months validating the software. Nobody had spent a single hour validating the AI doing the testing.
Here's the uncomfortable truth about AI systems in a quality engineering context: they fail differently from the software we've been trained to test.
When a deterministic function fails, there's a trail. Something breaks, an error gets thrown, a test goes red. The failure is findable.
When an AI system fails, nothing breaks. The model runs. The output comes back. The confidence score looks fine. And somewhere in that output is something wrong — a hallucination, a systematically missed defect category, a decision made on stale patterns — that no traditional test case was designed to catch.
I've spent my career getting very good at finding software defects. But finding defects in an AI testing tool requires a completely different set of skills:
You need to understand how probabilistic models fail, not just how deterministic code fails. You need to know what distribution shift looks like and how to detect it before accuracy degrades. You need to build adversarial test suites designed to elicit the specific failure modes most likely to appear in production. You need to think about confidence calibration, ground truth validation, and consistency testing — concepts that don't appear anywhere in traditional QA training.
Traditional QA prepared me to answer: did the software do what we expected?
AI validation requires me to answer: is the AI doing what we think it's doing, and how would we know if it wasn't?
Those are very different questions.
I've started calling it the AI Validation Engineer.
Not the person who builds the AI testing tool. Not the person who uses it day-to-day. The person who is accountable for answering the harder question: is this AI system actually doing what the organization believes it's doing?
The work exists whether or not the role does. Someone needs to:
Define what failure looks like for AI systems in their specific domain. A 3% hallucination rate means something very different in a product recommendation engine versus a clinical documentation system. The validation scope has to be domain-specific, not generic.
Build the testing infrastructure to detect AI failure modes. Ground truth validation pipelines. Consistency testing across semantically equivalent inputs. Adversarial test suites designed to probe the model's edges. Confidence threshold monitoring that catches degradation before it surfaces as customer complaints.
Own the human oversight layer. For high-stakes decisions — high-value transactions, regulatory touchpoints, content that could cause material harm — someone needs to define where human review is required and make sure that layer is actually working.
Monitor for distribution shift in production. The inputs an AI system sees in month six are not the same as the inputs it was trained on. That drift doesn't announce itself. Someone has to be watching.
Validate the AI tester itself. As AI tools increasingly assist with testing — generating test cases, identifying anomalies, prioritizing regressions — the recursive problem becomes unavoidable. You can't use an unvalidated AI system to validate your AI system. Somebody has to close that loop.
This is a full-time job. In most organizations, it's currently nobody's job.
The challenge with this role is that it requires a combination of capabilities that doesn't map cleanly onto any existing job description.
You need quality engineering experience to understand what good test coverage looks like and how to build validation infrastructure. You need enough machine learning knowledge to understand how AI systems fail and why confidence scores degrade. And you need enough domain expertise to define what failure actually means in your specific context.
Most QA engineers have the first. Few have the second. Almost none have been trained to combine all three.
That gap is uncomfortable if you're trying to hire for this role today. But if you're a quality engineer who's willing to develop the ML literacy to go alongside your testing fundamentals, it's one of the most significant career opportunities in the field right now.
The enterprises deploying AI in production need people who understand both sides of this problem. They need someone who can look at an AI testing tool and ask not just "is it running?" but "is it right?" — and build the infrastructure to answer that question with confidence.
I've been doing a few things practically to develop in this direction.
I've been pushing my published writing toward this intersection — exploring AI governance, validation frameworks, the specific ways AI systems fail in enterprise environments. The writing has forced me to think more rigorously about problems I'd been approaching intuitively.
I've been taking on peer review work for academic journals covering AI, machine learning, and software engineering — both to stay current with what the research community is actually working on and to build credibility in a space where credibility matters.
And I've been paying close attention to the gap between what AI testing tools promise and what they actually deliver in production — because that gap is where the AI Validation Engineer lives.
The job title doesn't appear on many org charts yet. But the work is already there, waiting for someone to own it.
If you're a QA engineer trying to figure out where the field is going, here's my honest take:
Automation didn't replace testers. It changed what testers needed to know. The engineers who thrived through the automation transition were the ones who learned to think about testing architecturally — not just executing test cases, but designing the frameworks and strategies that made testing scale.
AI is doing the same thing, at a higher level of abstraction.
The engineers who thrive through this transition won't be the ones who resist AI testing tools or over-rely on them. They'll be the ones who develop the judgment to evaluate AI behavior, the skills to build validation infrastructure, and the domain knowledge to define what good looks like in their specific context.
The AI Validation Engineer is the next evolution of what quality engineers have always done: making sure that the systems organizations trust are actually trustworthy.
The career shift is already happening. The only question is whether you're ahead of it or behind it.
None of those transitions felt as disruptive as what's happening right now.
AI is not just changing how we test software. It's creating an entirely new problem that quality engineering hasn't had to solve before: who validates the AI that's doing the testing?
That question is what pushed me to start thinking differently about my own career — and about where quality engineering is heading as a discipline.
The moment I realized something was off
About a year ago, I was reviewing test results from an AI-assisted testing tool that a team I was supporting had integrated into their pipeline. The dashboard was clean. Coverage metrics looked good. The model was flagging anomalies, prioritizing regression tests, and producing outputs that looked exactly like what we'd asked for.
But something bothered me.
I started digging into the cases the model was not flagging. I found a pattern. The model was consistently confident about a category of transactions that had been relatively stable for two years — but the product had just added a new payment processing rule that introduced a subtle edge case. The AI tester had never seen anything like it in its training data, so it wasn't looking for it.
The binary metrics said everything was fine. The AI was succeeding at testing the wrong things.
That's when it clicked. We had spent months validating the software. Nobody had spent a single hour validating the AI doing the testing.
What traditional QA doesn't prepare you for
Here's the uncomfortable truth about AI systems in a quality engineering context: they fail differently from the software we've been trained to test.
When a deterministic function fails, there's a trail. Something breaks, an error gets thrown, a test goes red. The failure is findable.
When an AI system fails, nothing breaks. The model runs. The output comes back. The confidence score looks fine. And somewhere in that output is something wrong — a hallucination, a systematically missed defect category, a decision made on stale patterns — that no traditional test case was designed to catch.
I've spent my career getting very good at finding software defects. But finding defects in an AI testing tool requires a completely different set of skills:
You need to understand how probabilistic models fail, not just how deterministic code fails. You need to know what distribution shift looks like and how to detect it before accuracy degrades. You need to build adversarial test suites designed to elicit the specific failure modes most likely to appear in production. You need to think about confidence calibration, ground truth validation, and consistency testing — concepts that don't appear anywhere in traditional QA training.
Traditional QA prepared me to answer: did the software do what we expected?
AI validation requires me to answer: is the AI doing what we think it's doing, and how would we know if it wasn't?
Those are very different questions.
The role that doesn't exist yet — but already has work
I've started calling it the AI Validation Engineer.
Not the person who builds the AI testing tool. Not the person who uses it day-to-day. The person who is accountable for answering the harder question: is this AI system actually doing what the organization believes it's doing?
The work exists whether or not the role does. Someone needs to:
Define what failure looks like for AI systems in their specific domain. A 3% hallucination rate means something very different in a product recommendation engine versus a clinical documentation system. The validation scope has to be domain-specific, not generic.
Build the testing infrastructure to detect AI failure modes. Ground truth validation pipelines. Consistency testing across semantically equivalent inputs. Adversarial test suites designed to probe the model's edges. Confidence threshold monitoring that catches degradation before it surfaces as customer complaints.
Own the human oversight layer. For high-stakes decisions — high-value transactions, regulatory touchpoints, content that could cause material harm — someone needs to define where human review is required and make sure that layer is actually working.
Monitor for distribution shift in production. The inputs an AI system sees in month six are not the same as the inputs it was trained on. That drift doesn't announce itself. Someone has to be watching.
Validate the AI tester itself. As AI tools increasingly assist with testing — generating test cases, identifying anomalies, prioritizing regressions — the recursive problem becomes unavoidable. You can't use an unvalidated AI system to validate your AI system. Somebody has to close that loop.
This is a full-time job. In most organizations, it's currently nobody's job.
The skill gap is real — and it's an opportunity
The challenge with this role is that it requires a combination of capabilities that doesn't map cleanly onto any existing job description.
You need quality engineering experience to understand what good test coverage looks like and how to build validation infrastructure. You need enough machine learning knowledge to understand how AI systems fail and why confidence scores degrade. And you need enough domain expertise to define what failure actually means in your specific context.
Most QA engineers have the first. Few have the second. Almost none have been trained to combine all three.
That gap is uncomfortable if you're trying to hire for this role today. But if you're a quality engineer who's willing to develop the ML literacy to go alongside your testing fundamentals, it's one of the most significant career opportunities in the field right now.
The enterprises deploying AI in production need people who understand both sides of this problem. They need someone who can look at an AI testing tool and ask not just "is it running?" but "is it right?" — and build the infrastructure to answer that question with confidence.
How I'm thinking about the transition
I've been doing a few things practically to develop in this direction.
I've been pushing my published writing toward this intersection — exploring AI governance, validation frameworks, the specific ways AI systems fail in enterprise environments. The writing has forced me to think more rigorously about problems I'd been approaching intuitively.
I've been taking on peer review work for academic journals covering AI, machine learning, and software engineering — both to stay current with what the research community is actually working on and to build credibility in a space where credibility matters.
And I've been paying close attention to the gap between what AI testing tools promise and what they actually deliver in production — because that gap is where the AI Validation Engineer lives.
The job title doesn't appear on many org charts yet. But the work is already there, waiting for someone to own it.
What this means for QA engineers right now
If you're a QA engineer trying to figure out where the field is going, here's my honest take:
Automation didn't replace testers. It changed what testers needed to know. The engineers who thrived through the automation transition were the ones who learned to think about testing architecturally — not just executing test cases, but designing the frameworks and strategies that made testing scale.
AI is doing the same thing, at a higher level of abstraction.
The engineers who thrive through this transition won't be the ones who resist AI testing tools or over-rely on them. They'll be the ones who develop the judgment to evaluate AI behavior, the skills to build validation infrastructure, and the domain knowledge to define what good looks like in their specific context.
The AI Validation Engineer is the next evolution of what quality engineers have always done: making sure that the systems organizations trust are actually trustworthy.
The career shift is already happening. The only question is whether you're ahead of it or behind it.