Walk into the interview already prepared.

Fresher (0–3 years): Junior MLOps Engineer

1. What is MLOps and why is it needed?
2. How would you deploy a trained scikit-learn model?
3. What would you monitor for a model in production?
4. What does MLflow help with?
5. Why version data as well as code?
6. A common way to serve a model as an API is…
7. What is data drift?
8. Batch vs real-time inference: batch is best when…
9. What is a model registry?

Mid-level (3–8 years): MLOps Engineer

1. Design an automated retraining pipeline.
2. Accuracy dropped last week. How do you investigate?
3. How do you operate an LLM application in production?
4. Which tools orchestrate multi-step ML pipelines?
5. Safely releasing a new model version often uses…
6. Training/serving skew means…
7. CI for ML should test…
8. KServe / Seldon on Kubernetes provide…
9. LLMOps adds which concern beyond classic MLOps?

Experienced (8–15 years): Senior MLOps / ML Platform Lead

1. Design an ML platform for 10 data science teams.
2. How would you set up LLMOps for a company with 20 GenAI apps?
3. How do you know your ML models are delivering value?
4. An ML platform should optimise for…
5. GPU cost is exploding. First lever?
6. Model governance in regulated industries needs…
7. A central LLM gateway gives…
8. Reproducibility at scale is mainly achieved by…
9. Multi-model serving on shared infrastructure helps…