Design an internal ML platform for 15 data science teams who currently deploy models in different ad hoc ways.

What the interviewer wants to hear

• Understand needs and pick paved paths, not one tool for every case
• Core components: data access, feature store, experiment tracking, registry, pipelines, serving, monitoring
• Self-service templates and golden paths
• Security, access control, governance, cost visibility
• Adoption strategy and measuring platform success

Model answer

I would start by interviewing teams to see their common workflows and pain points, then build a paved road for the most common cases rather than trying to support everything. The core pieces are: governed data access, experiment tracking, a central model registry, a pipeline orchestrator for training and batch jobs, a standard serving layer on Kubernetes for real-time models, a feature store where several teams share features, and built-in monitoring for latency, drift and quality. Teams get self-service templates, such as a project template with CI/CD, Docker and monitoring already wired, so a new model can reach staging in a day. Security and governance are built in: role-based access, secrets handling, lineage and audit logs. I show cost per team. I would migrate one or two willing teams first, measure time to deploy and incidents, then expand.

More hard MLOps Engineer questions

• How would you self-host and serve an open-weight LLM for thousands of concurrent users?
• Your recommendation model suddenly shows a 25% drop in click-through rate. Walk through your incident response.
• The ML infrastructure bill doubled in six months. How do you bring it down without slowing teams?
• A regulator asks you to show exactly how a credit decision model was built and how a past decision was made. How should your MLOps setup support this?
• You need to run a multi-day, multi-node training job on preemptible GPUs. How do you make it reliable and cost-effective?
• Your RAG system has 200 million chunks indexed with an old embedding model. How do you move to a new embedding model without downtime or quality loss?
• Users say a self-hosted LLM feature is too slow. How do you reduce latency without destroying throughput or quality?
• Under privacy law, a customer asks you to delete all of their data. How should an ML platform handle this across datasets, features, indexes and models?
• How do you build a real-time feature pipeline whose values match the batch features used in training?