How to vet technical talent in the age of AI
AI has changed what a resume, coding test, and first interview can prove. The hiring process now needs to evaluate demonstrated technical judgment, not just polished artifacts or keyword alignment.
Hiring signal has to move from artifact review to live judgment.
Resume signal is weaker
AI-assisted resumes can be polished, keyword-dense, and technically plausible. The hiring risk is not that candidates use AI; it is that the process stops before they have to explain trade-offs, failure modes, and production judgment.
Generic tests are easier to route around
Puzzle-style assessments and take-home templates are increasingly easy to rehearse, outsource, or solve with tooling. Senior technical hiring needs live reasoning, not just a score from a controlled exercise.
AI fluency is now part of the job
Strong engineers should know how to use AI tools without surrendering judgment. A modern assessment should reveal how they prompt, verify, debug, and decide when generated output is unsafe.
What to assess when AI is part of the workflow
The goal is not to ban AI. The goal is to see whether the candidate can use modern tools while still owning the technical decision. Vettara scorecards separate productivity from judgment.
- Architecture reasoning and trade-off clarity
- Debugging method under incomplete information
- Code quality instincts and maintainability judgment
- AI tool usage, validation habits, and independent thinking
- Communication, ownership, and collaboration patterns
- Risk flags that could affect ramp, retention, or delivery
A practical AI-era vetting process
Calibrate
Define the role bar, seniority expectations, stack context, and the decisions this hire must be able to make independently.
Deep-dive
Run a 60-90 minute live technical conversation led by a principal-level technologist, anchored in realistic production scenarios.
Observe AI usage
Let candidates use AI where relevant, then inspect how they frame prompts, validate answers, challenge assumptions, and recover from bad output.
Score
Convert the interview into a structured scorecard with evidence, hire/no-hire guidance, growth areas, and specific risk flags.
Red flags that matter more in AI-assisted hiring
AI can hide shallow experience during early screens, so the interview needs to watch for patterns that affect production reliability, communication, and ownership after the hire is made.
- Cannot explain the reasoning behind an apparently correct answer.
- Treats AI output as authoritative instead of something to verify.
- Optimizes for cleverness while ignoring operational reliability.
- Avoids concrete details about past technical ownership.
- Struggles to adjust communication depth for technical and non-technical stakeholders.
Principal-led vetting gives hiring teams a cleaner signal.
Vettara runs live technical deep-dives for teams that already have candidates, and retained technical search for teams that need vetted shortlists. In both cases, the same protocol turns interview evidence into a decision-ready scorecard.