How to Conduct Technical Interviews for AI Engineering Roles

Hiring AI engineers is no longer just about checking if someone can code. The stakes are higher, the skill sets are deeper, and the interview process must evolve. From deep learning to MLOps, a successful AI engineering hire needs a blend of theory, coding, architecture, and communication skills.

That’s why technical interviews for AI engineers require a bespoke strategy—one that tests both the depth and breadth of the candidate’s capabilities. Here’s how you can conduct a hiring process that filters for excellence—without bias or guesswork.

Step 1: Define the Role’s Core Requirements

Start by understanding the specific AI role. Not every position demands the same depth in deep learning or statistical modeling. Break down needs into categories like:

• Machine Learning Engineer: Feature engineering, model deployment, pipelines
• Deep Learning Engineer: CNNs, RNNs, transformers, PyTorch or TensorFlow
• Data Scientist: Hypothesis testing, business insights, data visualization
• MLOps Engineer: Model CI/CD, Kubernetes, cloud AI infrastructure

This clarity will shape your interview structure, preventing scope creep and irrelevant questions.

Step 2: Create a Multi-Phase Interview Process

A solid AI interview should include these phases:

1. Technical Screening (Coding and Math)

Use platforms like HackerRank, Codility, or Kaggle notebooks to test:

• Python or R proficiency
• Data manipulation (NumPy, pandas)
• Algorithmic problem-solving (trees, graphs, matrix ops)

2. Theoretical Assessment

Ask conceptual questions to evaluate knowledge of:

• Supervised vs unsupervised learning
• Bias-variance tradeoff
• Regularization techniques
• Loss functions and optimization

3. Practical Case Study or Mini Project

Give a realistic problem:

• Predicting churn, fraud, or anomalies
• Cleaning and modeling a real-world dataset
• Designing an AI-powered feature with constraints

Allow 2–3 days. Assess for creativity, data cleaning, and model choice—not just accuracy.

4. System Design and MLOps

For senior roles, test:

• Model lifecycle awareness
• Tools like MLflow, Kubeflow, Airflow
• Knowledge of CI/CD, monitoring, versioning

Ask them to diagram a deployment architecture or explain A/B testing pipelines.

5. Soft Skills and Communication

Can they explain AI decisions to non-technical stakeholders?
Do they ask clarifying questions and document clearly?
AI isn’t just math—it’s about storytelling with data.

Top Questions to Ask in AI Engineering Interviews

• “How would you handle class imbalance in a classification problem?”
• “Explain dropout in neural networks and why it’s used.”
• “How do you prevent data leakage in your ML pipeline?”
• “Walk me through how you’d deploy a model to production securely.”
• “What’s your process for evaluating model performance?”

Evaluation Tips for Interviewers

• Use a rubric to score each phase consistently. Include coding quality, problem-solving, communication, and model thinking.
• Have multiple interviewers to avoid unconscious bias.
• Allow candidates to ask questions. Their curiosity shows engagement.
• For remote roles, test async work and documentation skills too.

Common Mistakes to Avoid

• Over-focusing on coding puzzles: Real AI work is more than reversing a linked list.
• Ignoring ethical awareness: Can they spot bias or explain model fairness?
• Skipping real-world scenarios: Academic knowledge doesn’t always translate.
• Underestimating collaboration: AI engineers work in teams. Evaluate team fit.

How Loopp Supports Technical Interviews for AI Hiring

At Loopp, we equip recruiters and hiring managers with:

• Pre-vetted AI engineers with diverse portfolios
• Interview-ready questions based on your tech stack
• Custom take-home assignments tailored to your use case
• End-to-end support in interview scheduling, evaluation, and decision-making

Hiring doesn’t have to feel like a guessing game. With our network, tools, and templates, you’ll make better AI hires—faster.

Conclusion: Build Smarter by Hiring Better

The success of your AI initiatives depends on hiring the right minds. But identifying those minds takes more than a résumé scan or algorithm quiz. It takes structured, intelligent interviewing—designed to reveal the full potential of each candidate.

So the next time you sit down to interview an AI engineer, don’t just test what they know. Discover how they think, how they code, and how they communicate. And if you need help building the framework, Loopp is your partner in AI hiring done right.

Ready to upgrade your AI interviews and source exceptional talent? Get started with Loopp today.