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.

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.