What do AI interviews look for?

In AI interviews, hiring teams typically look for a combination of technical expertise, problem-solving ability, and understanding of AI fundamentals. Here's a breakdown of what AI interviews focus on:

1. Technical Knowledge

AI interviews assess your depth of knowledge in the following areas:

• Machine Learning (ML): You should be familiar with supervised, unsupervised, and reinforcement learning techniques. Key algorithms such as linear regression, decision trees, support vector machines, and neural networks are often discussed.
• Deep Learning: Understanding of neural networks, CNNs, RNNs, and how frameworks like TensorFlow and PyTorch work is critical.
• Mathematics: Proficiency in key mathematical concepts that underlie AI, such as linear algebra, calculus, probability, and statistics, is expected. These concepts are fundamental to understanding how models learn and make predictions.
• NLP and Computer Vision: Depending on the role, knowledge in specific areas like natural language processing (NLP) or computer vision could be crucial.

2. Problem-Solving and Coding

AI roles often require strong coding skills, particularly in languages like Python. Interviews may include:

• Coding Problems: You’ll be asked to solve coding problems that test your ability to implement machine learning models, work with data structures, or optimize algorithms. Platforms like LeetCode are often recommended for practice.
• Model Implementation: You may be asked to implement or explain a machine learning model from scratch or adjust existing models to suit a particular problem.

3. System Design and Scalability

For more senior roles, you might encounter AI system design questions. You’ll need to demonstrate your ability to:

• Design scalable AI systems that handle large amounts of data (e.g., distributed training on GPUs).
• Choose between SQL vs. NoSQL databases, and optimize for scalability and performance.

4. Applied AI and Real-World Experience

Interviewers often want to know how you have applied AI in previous projects or real-world applications:

• Project Experience: Be ready to talk about any AI projects you’ve worked on, the models used, the data you dealt with, and how you overcame challenges.
• Deployment and Monitoring: Understanding how to deploy AI models in production and monitor their performance over time is increasingly important, especially in ML Ops roles.

5. Behavioral and Communication Skills

Communication is vital for AI roles, especially when explaining complex AI concepts to non-technical stakeholders. Expect questions about:

• Collaboration: How you work in teams, particularly with cross-functional partners like data engineers or product managers.
• Problem-Solving Under Pressure: How you handle tight deadlines or solve ambiguous problems.

For comprehensive preparation, courses like Grokking the Coding Interview and Grokking System Design can help, along with mock interviews that simulate real AI interview scenarios.