Walking into an interview without a loaded gun of questions is negligent. You need to pierce the sales veneer to see the engineering rot underneath.
1. 'How do you handle data drift post-deployment?'
Why: Models decay. If they don't have a plan for AI in app development maintenance, they are selling you a car with no engine oil.
2. 'Who owns the weights and biases of the trained model?'
Why: Code is cheap; the trained brain is the asset. Ensure you own the neural network, not just the API access.
3. 'What is your specific stack for handling unstructured data?'
Why: Structured data is easy. The real world is messy. You need to know if they can handle the chaos of reality.
4. 'Can you demonstrate your automated testing pipelines for non-deterministic outputs?'
Why: Traditional QA fails here. You need specialized AI in software testing protocols to verify answers that change every time.
5. 'How do you mitigate algorithmic hallucinations?'
Why: A lying AI is a liability. You need concrete strategies for grounding the model in factual reality.
6. 'What is your strategy for low-latency inference?'
Why: Accuracy doesn't matter if the user has to wait ten seconds. Speed is a feature.
7. 'How do you tackle cold-start problems in recommendation engines?'
Why: This reveals their depth in AI in personalization strategies when data is scarce or nonexistent.
8. 'Can you walk me through a project where you failed?'
Why: Success is often luck; failure is where the lessons are learned. Trust the scar tissue.
9. 'How do you integrate ethics into your development lifecycle?'
Why: Bias can destroy your brand. You need an agency that codes with a conscience.
10. 'Do you fine-tune foundation models or train from scratch?'
Why: This dictates the budget. Fine-tuning is surgical; training from scratch is nuclear.
