MLOps Engineer Interview Questions UK: 8 Real Questions and What Interviewers Want

Before your MLOps Engineer interview, it helps to see the questions from the interviewer's side. We have picked eight from our set of 16 and added the reason each one gets asked.

Eight MLOps Engineer interview questions, and what the interviewer is thinking

1. How would you design a CI/CD pipeline for machine learning models?

Why they ask: This is the defining MLOps question and tests end-to-end thinking beyond software CI/CD.

2. What is model drift, and how do you detect and respond to it in production?

Why they ask: Monitoring for drift is one of the most common MLOps topics.

3. What is a feature store, and when is it worth introducing one?

Why they ask: Feature stores are a standard MLOps component and a common point of debate.

4. How do you containerise and serve a model, and how do you choose between batch, real-time and streaming inference?

Why they ask: Deployment patterns are central to the job and they want practical trade-off thinking.

5. What would you track in an experiment tracking and model registry setup, and why does reproducibility matter?

Why they ask: Reproducibility and traceability are core to MLOps and audit.

6. A production model's accuracy has dropped sharply overnight. What do you do?

Why they ask: They want a calm, structured incident approach for ML systems.

7. Tell me about a time you automated a manual machine learning or data process.

Why they ask: Automation is the heart of MLOps and they want measurable outcomes.

8. How is MLOps different from DevOps and data science, and why does it interest you?

Why they ask: They test understanding of the role's boundaries and your motivation.

A sample answer to question 1

How would you design a CI/CD pipeline for machine learning models?

ML pipelines must version code, data and models. I would use Git for code, a data versioning approach such as DVC or lakehouse table versions, and an experiment tracker such as MLflow or Weights and Biases. On a pull request, CI runs linting, unit tests for feature logic, data validation with Great Expectations or Pandera, and a small training run to catch breakage. When merged, an orchestrated training pipeline, in Azure ML, SageMaker, Vertex AI, Kubeflow or Airflow, trains the model on versioned data, evaluates it against a held-out set and the current production model, and registers it in a model registry only if it passes agreed thresholds, including fairness checks. Deployment is a separate, automated step: package the model in a container, deploy to staging, run integration and load tests, then release to production using canary or shadow deployment with automatic rollback. Infrastructure is defined as code with Terraform. Monitoring closes the loop, and drift or degradation triggers retraining.

Common mistake to avoid: Treating it like standard software CI with no data tests; manual promotion of models

Want the rest?

Full model answers, key points and common mistakes for all 16 questions are on the MLOps Engineer interview questions page. The first five are free, no sign-up needed.