Skit.ai MLOps Engineer Interview: Rounds, Questions and Preparation

What the job involves

An MLOps Engineer at AI and deeptech companies like Skit.ai builds and maintains the infrastructure that takes machine learning models from experimentation to production. Day to day, you automate training pipelines, deploy models as scalable APIs, monitor for drift and performance issues, and collaborate with data scientists and software engineers to ensure reliable, reproducible ML systems. This role sits at the intersection of machine learning, DevOps, and cloud engineering.

Role and Technical Knowledge

1. 1

   What is the difference between DevOps and MLOps?

   Highlight that MLOps adds data and model versioning, experiment tracking, continuous training, and model monitoring to standard DevOps practices. Mention that ML systems have additional failure modes like data drift and concept drift.

2. 2

   Explain how you would version a machine learning model and its associated data.

   Cover tools like DVC for data versioning, MLflow or Git LFS for model artifacts, and the importance of linking model versions to training data and code commits. Emphasise reproducibility.

3. 3

   What is a feature store and why is it useful in production ML?

   Describe it as a centralised repository for curated features, ensuring consistency between training and serving. Mention benefits like reuse, discovery, and avoiding training-serving skew.

4. 4

   How does Kubernetes help in deploying ML models?

   Talk about container orchestration, scaling, rolling updates, and self-healing. Explain how you would package a model as a Docker image and deploy it as a Kubernetes deployment with a service.

Practical Scenarios

1. 5

   A model that performed well in testing is now giving poor predictions in production. How would you debug this?

   Check for data drift, training-serving skew, pipeline bugs, and changes in input distribution. Suggest comparing production data statistics with training data and setting up monitoring alerts.

2. 6

   You need to deploy a new model version without downtime. What strategy would you use?

   Mention blue-green deployment or canary release, where you route a small percentage of traffic to the new model and monitor metrics before full rollout. Highlight rollback plans.

3. 7

   How would you set up a CI/CD pipeline for a machine learning project?

   Describe stages: code linting and testing, data validation, model training and evaluation, model registration, and deployment to staging/production. Use tools like Jenkins, GitHub Actions, or GitLab CI with MLflow.

4. 8

   Your model inference latency is too high. What steps would you take to reduce it?

   Consider model optimisation (quantisation, pruning), batching requests, using a faster runtime (ONNX, TensorRT), and scaling horizontally. Also check for inefficient pre-processing code.

System Design and Architecture

1. 9

   Design an end-to-end ML pipeline for a voice bot that handles customer queries.

   Cover data ingestion, preprocessing, model training (ASR, NLU), model registry, deployment as microservices, and monitoring for accuracy and latency. Mention scalability and fault tolerance.

2. 10

   How would you monitor a deployed model in production?

   Discuss tracking prediction distribution, input feature drift, latency, throughput, and business metrics. Suggest tools like Prometheus, Grafana, and Evidently AI, and setting up alerts.

3. 11

   What components would you include in a feature engineering pipeline for real-time inference?

   Include data validation, transformation, feature computation, and storage in a low-latency store like Redis. Emphasise consistency with training features and handling missing values.

4. 12

   How would you ensure reproducibility of an ML experiment?

   Use version control for code, data, and environment (Docker), track parameters and metrics with MLflow or Weights & Biases, and set random seeds. Document the entire pipeline.

Behavioural and HR

1. 13

   Tell me about a time you had to learn a new technology quickly for a project.

   Use the STAR method. Focus on the steps you took to learn, how you applied it, and the positive outcome. Show enthusiasm for continuous learning.

2. 14

   Describe a situation where you had to work with a team to solve a technical problem.

   Highlight collaboration, communication, and your specific contribution. Mention how you handled disagreements or integrated feedback.

3. 15

   Why do you want to work as an MLOps Engineer at an AI-first company like Skit.ai?

   Show passion for AI and infrastructure, and connect your skills to the company's mission. Mention interest in solving real-world problems with voice AI and scaling ML systems.

4. 16

   How do you handle tight deadlines or pressure?

   Give an example of prioritising tasks, staying calm, and delivering. Emphasise planning, communication with stakeholders, and focusing on critical path items.