MLOps Engineer
Practice MLOps: ML pipelines, model registry, CI/CD for models, serving on Kubernetes and GPUs, monitoring, drift, LLM serving and cost control.
- Questions
- 100
- Interview Qs
- 30
- Judged problems
- 16
- Skills
- 9
About the role
An MLOps Engineer builds the platform and processes that take machine learning models from a notebook to reliable production systems. You make training repeatable, models versioned and deployments safe, so data scientists and ML engineers can ship faster without breaking things.
Day to day you build training and data pipelines, set up experiment tracking and model registries, write CI/CD that tests and deploys models, and run serving infrastructure on Kubernetes, often with GPUs. You add monitoring for latency, errors, data drift and model quality, and you handle incidents when a model starts behaving badly.
The role mixes DevOps, data engineering and ML knowledge. In 2026 it also includes serving large language models efficiently, managing GPU cost, securing model artifacts and keeping lineage records for audits and governance.
Practise by level
Open the full question bank (100 questions) →Mock interview
Screening round
4 questions · 4 min each · pass 50%
mediumTechnical round
5 questions · 4 min each · pass 60%
hardFinal round
6 questions · 3 min each · pass 70%
Coding problems for mlops engineers
- Binary Classifier Metricseasy
- First Failing Buildeasy
- How Much Error Budget Is Left?easy
- Latency Percentileseasy
- Min-Max Feature Scalingeasy
- Best Match by Cosine Similaritymedium
- Fewest Terms to Finish a Programmedium
- K Closest Feature Vectorsmedium
- Longest Uptime With K Repairsmedium
- Minimum Upload Capacitymedium
- One Step of K-Meansmedium
- Plan Courses Smallest-Firstmedium
Skills in this track
Learning roadmap
- 1
Software and Linux basics
Be confident with Python, Bash, Git and Linux. Learn how web services, APIs and networking work.
- 2
Containers and cloud
Package apps with Docker, learn Kubernetes basics (pods, deployments, services, autoscaling) and one major cloud provider.
- 3
ML lifecycle tools
Use experiment tracking, a model registry, data versioning and pipeline orchestrators to make training reproducible.
- 4
Serving and CI/CD
Serve models as APIs and batch jobs, build CI/CD that tests data, code and models, and roll out with canary and shadow deployments.
- 5
Monitoring, scale and cost
Monitor latency, drift and model quality. Learn GPU scheduling, LLM serving engines, autoscaling and cost optimisation.
Top MLOps Engineer interview questions
- What is MLOps, and how is it different from traditional DevOps?
- Why does a team need a model registry? What information should it store?
- How do you make a model training run reproducible?
- How would you deploy a trained scikit-learn model as a REST API?
- What would you monitor for a model running in production?
- What is training-serving skew, and how do you prevent it?