You hired an AI engineer who looked amazing on paper. Three months later, nothing in production. Sound familiar?
That’s not a people problem. It’s a process problem.
In this video, Andrea from Tesoro AI walks through the 4-Layer Framework we use to identify AI talent who can actually ship. You’ll learn why resumes lie about AI capability, what separates a notebook engineer from a production engineer, and why cultural alignment predicts retention better than compensation.
Chapters:
0:00 – Hook: Have you ever hired an AI engineer who couldn’t ship?
0:30 – Why most companies make bad AI hires
1:10 – Why resumes lie about AI capability (the LLM example)
2:15 – Why AI hiring is different from regular software engineering
3:55 – What a bad AI hire actually costs (3-6 months of salary and beyond)
5:15 – What we test in Technical Depth that coding challenges miss
6:50 – Notebook engineer vs. production engineer (the key distinction)
8:20 – Red flags and green flags in AI candidate interviews
10:30 – Why communication matters more than you think for AI roles
11:30 – Why cultural alignment predicts retention more than compensation
12:25 – How all 4 layers show up in the shortlists we deliver
13:10 – CTA: See this process in action
The 4-Layer Framework:
- Technical Depth: architecture thinking, not puzzle-solving
- Production Experience: can they ship to the real world?
- Communication: can they explain complex AI to non-technical teams?
- Cultural Alignment: compensation gets people in the door. Alignment keeps them.
We send 3-5 candidates who passed all four layers. Not 20-40 resumes to filter.
Want to see this process in action?
More from Tesoro AI:
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