The best engineers treat AI like a really solid junior developer—they know how to guide it, when to trust it, and when to step in themselves. They write clear prompts, quickly spot when AI goes off track, and can walk through every line of generated code.

AI-dependent engineers, on the other hand, tend to accept whatever the model outputs without fully understanding it. They struggle when AI produces incorrect solutions and have difficulty modifying code when requirements change.

You want engineers who use AI as a force multiplier for their expertise, not as a replacement for understanding fundamentals.

In the pre-AI world, interviews tested whether candidates could produce correct answers under artificial constraints. Now you need to evaluate their problem-solving methodology when they have access to real tools.

Watch how they break down problems, structure their approach, and iterate toward solutions. Do they validate AI outputs critically? How do they handle edge cases the AI missed? Can they debug when something goes wrong?

AI can solve immediate coding needs quickly, but engineers need to think beyond the quick fix. We need people who can evaluate not just whether code works, but how solutions are organized and structured for long-term success—performance implications, security considerations, and eventual scalability challenges.

The best engineers use AI to handle immediate implementation while keeping their focus on the bigger picture: building systems that won't become technical debt down the road.

This isn't just about catching bugs—it's about maintaining high standards when the pressure to ship fast is intense. The best engineers are rigorous reviewers of their own and others' work, even when AI makes it tempting to skip thorough evaluation.

Give candidates a realistic feature-building task using whatever tools they'd normally use. Start them off with an existing codebase sample (potentially AI-generated itself) that has a subtle bug or issue they need to find and fix, then have them build a new feature on top of it.

This mirrors actual development work—you're often inheriting code (AI-generated or otherwise) that needs debugging before you can extend it. The exercise combines code review skills with feature development, just like the real job.

What to watch for:

• Do they spot the initial issue in the provided code?

• How do they break down the problem before jumping into implementation?

• Are their AI prompts clear and specific?

• Do they test their solutions properly?

• How do they handle when AI gives them something that doesn't work?

• Can they explain their decisions without getting too in the weeds?

Red flags:

• Missing obvious bugs in the starter code

• Just copy-pasting AI suggestions without understanding them

• Can't debug when things break

• No clear approach to testing or validation

• Gets lost explaining their choices

Make it collaborative—engage with their process, ask follow-up questions, see how they handle discussion about their technical decisions.

This is where we remove AI support entirely. While AI can suggest architectural patterns and solutions, system design interviews test whether candidates can think strategically and independently about complex problems. These are the foundational skills they'll need to guide AI toward the right architectural decisions in real-world scenarios.

We need talent who can establish the guardrails and constraints that direct AI tooling toward desired outcomes. Without this independent thinking ability, engineers become dependent on AI suggestions rather than using AI as a tool to implement their vision.

What to watch for:

• Do they break down complex systems into manageable components?

• Can they identify potential bottlenecks and failure points?

• How do they handle trade-offs between different architectural approaches?

• Do they consider operational concerns like monitoring and deployment?

Red flags:

• Over-engineering simple problems or under-engineering complex ones

• Can't explain why they chose specific technologies or patterns

• Ignores real-world constraints like team size or timeline

• Focuses only on happy path scenarios

Strong candidates understand that when AI handles more of the implementation work, they can focus more time on getting the architecture right and spending time with users to make sure they're building the right thing.

At Lazer Technologies, we've started incorporating AI tools into our technical interviews. While we're still figuring out what works best for our team, this approach has given us better insights into how candidates actually work with AI versus being overly dependent on it.

What we've learned is that the fundamentals still really matter. The engineers who perform best aren't necessarily the ones who use AI the most—they're the ones who combine AI effectively with solid engineering principles. They validate solutions, write good tests, and can clearly explain their technical decisions.

The space is evolving rapidly, but early signs are promising. We're getting a much clearer picture of how candidates will actually perform on the job.

Stop testing for skills that AI has commoditized. Start testing for the abilities that become more valuable when combined with AI: problem decomposition, solution validation, architectural thinking, and quality gatekeeping.

The best engineers in the AI era aren't the ones who can code without AI—they're the ones who can build better software faster by leveraging AI effectively while maintaining high standards. Your technical interviews should reflect this reality.

AI is a tool, not a solution. You need engineers who know how to use that tool well while still thinking critically, reviewing thoroughly, and designing systems that scale. Test for those skills, and you'll hire the engineers who can thrive in the AI-powered future.

The technology landscape moves fast, and talent practices need to move with it. This framework works today, but the key isn't having the perfect interview process—it's staying close enough to how engineers actually work that you can evolve your hiring alongside the technology. The strongest builders are always adapting to new tools and environments. Your talent practices should too.

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