Eugene Eruslanov

Is "Years of Experience" the Most Boomer Barrier in AI Hiring? Today I was contacted about a role that required 10 to 15 years of experience designing and deploying AI solutions. Let’s get real. That number is fiction. Ten to fifteen years ago, we didn’t have transformers. We didn’t have modern deep learning at scale. We weren’t talking about agentic systems. And we sure as hell weren’t deploying generative models into live production environments for real-time decision-making across businesses. So what does that requirement really measure? It’s not capability. It’s not vision. It’s not even relevance. It’s laziness. It's ignorance. In the past three years, I have worked exclusively on building applied AI solutions that meet real business needs. I’ve done it end-to-end: from data architecture to model development to system deployment. I’ve already built a fully agentic system that runs a small business autonomously, managing both internal operations and customer-facing processes. But because I can’t slap a fictional number next to “YOE,” I’m being passed over for roles (that I'm wildly over-qualified for 🙄) that are still stuck thinking AI innovation follows a traditional resume timeline. Let me be clear: I am not an edge case. This is happening to a whole generation of developers, researchers, and product leaders who have put in intense, focused years solving real problems with real AI. We are being filtered out by systems that measure tenure instead of impact, and HR teams that cannot distinguish automation from artificial intelligence. Meanwhile, the same companies that write these job descriptions are in a full sprint to “catch up” on AI. They don’t even know what they’re chasing. You’re asking for experience that doesn’t exist, while the people actually building this future are being overlooked, undervalued, or ghosted and they're creating their own solutions to destroy your industries. Recruiters: if you’re working on AI hiring and you don’t understand the tech, that’s fine—but get someone in the room who does. Executives: if your organization is treating AI as a checkbox or a panic response, you are bleeding time, money, and trust trying to chase a finish line you haven’t even defined. The apply-interview-hire system is broken. It prioritizes false signals and outdated filters. It rewards paper resumes over real-world innovation. This is not just a missed opportunity. It’s a systemic failure. If we want AI to lead to a better, more equitable, more intelligent future, we need to radically rethink how we hire the people building it. Gatekeepers are not protecting standards, they’re protecting the status quo. That’s the exact opposite of what this moment demands. #AIHiring #AgenticAI #EthicalAI #AILeadership #ResponsibleAI #TechWithPurpose #HiringInAI #GenZTech #JobSearch #AutomationVsAutonomy #YOEMyth #DataDrivenHiring

Mentoring a remote engineer is harder than mentoring someone at the next desk. But it might be more impactful. When you mentor someone in person, a lot happens by osmosis — they overhear conversations, watch how you debug, absorb your decision-making through proximity. Remote mentoring has to be intentional. Here's what I do: → Scheduled 1:1s with actual agendas (not just "how's it going") → Think-aloud sessions where I share my screen and narrate my reasoning through a real problem → Assign stretch tasks with guardrails — challenging enough to grow, supported enough to succeed → Written feedback on PRs that explains the WHY, not just the fix One offshore developer I mentored went from struggling with basic Magento module development to independently architecting a complex payment integration within 8 months. That growth didn't happen by accident. It happened because someone was intentional about it. #Mentoring #EngineeringLeadership #RemoteWork #CareerDevelopment