20 years in tech, still collecting certifications to prove it?
An architect chasing AI engineering roles told me why that fails.
I spoke with a senior architect this week — 20+ years across Java, cloud, and microservices. I’ll call him Sam.
Sam is preparing for a major AI certification right now. So what he said next carries some weight:
“Certification is scratching the surface. It’s not the real meat.”
He’s been on both sides of the interview table for two decades. His observation: “I’ve seen profiles with 20 certifications.” And he knows exactly what the other side actually probes for.
The meat, in his words: “Playing with the code, putting things together, writing end-to-end projects.”
Here’s why this matters double in AI engineering.
A certification proves you studied the material. An AI engineering interview probes what you built.
How does your RAG pipeline chunk documents? What did your evals catch before production? What does your agent system cost to run per day?
There is no study guide for those answers. They only come from building something end to end and living with the consequences.
Certifications still have a place. I tell our members to pick up the entry-level AWS AI certification — about a week of effort, and a good signal on a resume.
Then I tell them where it belongs: next to the projects. Never instead of them.
Sam figured this out after 20 years of watching certified people get filtered out in the first technical conversation.
You can skip the 20 years and just borrow the lesson: build first. Certify along the way.
Kirill
P.S. The AI Engineering Program is built on the meat — seven end-to-end projects, deployed to production. If that’s how you’d rather learn, let’s talk.