Hiring engineers has always been difficult, but the rise of AI tools like ChatGPT has changed the game completely.
Candidates can now generate clean, structured answers in seconds. Many sound impressive in interviews, yet struggle when they face real production challenges.
AI tools make it easier for candidates to prepare and polish their answers. That is not bad by itself, but it creates new problems for hiring managers.
The core issue is simple: we start rewarding confidence instead of competence. That is why hiring now requires a completely different lens.
We do not forbid AI use during interviews. Instead, we observe how candidates use it.
Strong engineers treat AI as an assistant, not a crutch. They read, analyze, and improve its output.
Weak engineers copy what it gives them and hope for the best.
Here is what we look for:
Real engineers reason in context. They use AI intelligently, but they never outsource their judgment.
To separate real thinkers from memorized responses, we design questions that add friction and mimic real work. Here are some examples.
“A zero-day vulnerability forces you to rotate JWT signing keys immediately. Thousands of users still have active sessions. How do you deploy new keys, keep users online, and revoke the compromised ones?”
This question tests reasoning about migration, backward compatibility, and operational safety.
“You said you want zero downtime, but you also need to change the database schema. How would you handle that?”
The goal is not a perfect answer. The goal is to see how the candidate balances performance, risk, and practicality.
“Walk me through the first 15 minutes after you detect an incident. What do you check first? What comes next?”
Good engineers think sequentially and prioritize. Weak ones describe theory without a timeline.
“Your autosave feature causes double submissions under load. How would you profile and fix it without breaking audit logs?”
This shows who can handle messy production scenarios.
Introduce new information or constraints halfway through the discussion. The right candidates adjust immediately. The wrong ones freeze.
Even with good questions, the real signal often comes from small behavioral cues.
Red flags:
Green flags:
We look for thinking patterns, not perfect scripts.
A common mistake in hiring is designing interviews that have nothing to do with the actual job.
Our engineers face scaling issues, debugging sessions, and trade-off decisions every day. So our interviews should test for the same.
Here is what helps:
When interviews feel like real engineering, both sides win.
We know that hiring needs to move fast. But depth should never disappear.
Here is how we balance both:
This way we maintain quality while keeping the process efficient.
When companies keep hiring for polish instead of substance, problems appear fast.
Hiring mistakes compound over time. Fixing them early is much cheaper than fixing code later.
At Coding Partners, our hiring philosophy is simple. We want engineers who can reason, adapt, and own their work.
AI can help them write faster, but it cannot replace their judgment.
If you are a tech leader, ask yourself:
True engineering strength shows up not in perfect answers, but in how people think when things break.
