Dennis Nerush

AI has fundamentally changed how engineers work, but most hiring processes haven't changed at all.

Coding assistants can generate solutions in seconds. System design answers can be refined with prompts. Candidates walk into interviews with powerful AI tools at their side. Yet many companies are still evaluating engineers as if AI didn't exist.

So how do you actually assess engineering ability in this new reality?

After interviewing hundreds of engineers at Elementor, I redesigned my hiring process for the AI era. Instead of trying to prevent candidates from using AI, I changed what we evaluate. Today we test how engineers think, how they collaborate with AI, and how they apply judgment when AI-generated solutions are incomplete, incorrect, or misleading.

In this talk, I'll share what changed in our hiring process and what actually works in practice:
how we evaluate coding ability when AI is involved, which system design questions reveal AI orchestration skills, and what behavioral signals distinguish high-impact engineers from candidates who rely on AI to mask gaps.

Whether you're hiring engineers, preparing for interviews, or leading an engineering organization, you'll leave with a practical framework for evaluating engineering talent in the AI era.

AI has completely changed how software engineers work. But interview processes haven't caught up.
Coding assistants solve LeetCode problems in seconds. AI can generate polished system design answers. Almost every candidate walks into an interview with powerful AI tools, yet many companies are still trying to evaluate engineers as if none of this exists.

So what actually separates exceptional engineers from everyone else?

After interviewing hundreds of engineers at Elementor, I realized we had to stop testing who could write code without AI and start testing who could build, reason, and make decisions with it.

In this talk, I'll pull back the curtain on what modern engineering interviews actually measure. You'll see why some candidates fail despite using AI effectively, why others succeed even when AI makes mistakes, and what signals experienced interviewers look for beyond generated code.

Whether you're interviewing for your next role or redesigning your hiring process, you'll leave knowing which skills matter now, how to demonstrate them during interviews, and why engineering judgment has become more valuable than ever.

Key Takeaways
1. Why traditional engineering interviews are breaking down

Understand how AI tools have changed the signal we get from coding interviews and why many common interview formats no longer evaluate the right skills.

2. What actually matters in engineering interviews today

Learn which capabilities distinguish strong engineers in the AI era, including technical judgment, problem framing, and system-level thinking.

3. How to evaluate candidates when AI is part of the process

See practical approaches for assessing coding ability, system design skills, and engineering decision-making when candidates are using AI tools.

4. Signals that reveal real engineering strength

Discover the behavioral and technical indicators that distinguish high-impact engineers from candidates who rely on AI to compensate for weak fundamentals.

5. How candidates can stand out in AI-era interviews

Understand how engineers can demonstrate their ability to use AI effectively while still showing strong engineering thinking and ownership.

6. A practical framework for modern engineering interviews

Leave with a concrete approach for redesigning interview processes to evaluate the skills that matter in the AI era.

What happens when generative AI isn't just a buzzword but a real engineering teammate? Over the past year, I've led my engineering team through a radical shift-integrating GenAI tools and building our own custom AI-powered solutions. The result? We've multiplied productivity, cut delivery times, and unlocked new creative workflows that were impossible before.In this talk, I'll share real-world examples of how we use tools like Cursor and Devin, as well as custom-built AI integrations, to speed up coding, automate testing, improve documentation, and even resolve complex bugs in minutes. I'll break down the process changes that made AI adoption successful-how we rewired our workflows, onboarded the team, and learned to "pair program" with AI effectively. Expect practical lessons and battle-tested insights: what worked brilliantly, what failed (and why), and the cultural shifts needed to make GenAI a force multiplier instead of just another shiny tool. Whether you're a team lead, engineer, or product builder, you'll walk away with actionable ideas to supercharge your own team using GenAI-without losing the human touch that drives great engineering.