Welcoming questions
- Can you tell us a bit about your background and experience in AI engineering?
- What initially attracted you to the field of artificial intelligence, and what motivates you to work in this area?
Role-specific / technical competencies
- Describe your experience with machine learning frameworks. Which ones have you used, and what projects have you implemented using them?
- Can you walk us through a complex AI project you have worked on? What were the challenges, and how did you overcome them?
- Explain how you would approach building a recommendation system from scratch. What key components would you consider?
Behavioural & culture fit
- Describe a situation where you faced a significant technical challenge. How did you handle it, and what was the outcome?
- How do you stay current with the latest developments in AI and machine learning? Can you give an example of how you've applied something new you learned?
- How do you prioritize your workload when working on multiple AI projects? Can you provide an example of a time when you had to manage competing deadlines?
Problem-solving / case
- Imagine you have been tasked with improving the accuracy of a predictive model that is consistently underperforming. What steps would you take to identify and address the issues?
- Suppose you are given a dataset with missing values and outliers. Describe how you would approach cleaning and preparing the data for model training.
- Consider a scenario where your AI model generates biased results. What methods would you use to detect bias in your data or algorithm, and how would you address it?
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