Towards AI•3 days ago
101 ML/LLM/Agentic AIOPS Interview Questions.
Last Updated on February 19, 2026 by Editorial Team Author(s): Niraj Kumar Originally published on Towards AI. Image by Author Section 1: Technical & Hands-On (ML/AI & MLOps) These questions test your foundational knowledge of MLOps, regardless of the cloud platform. 1. Describe the most complex ML project you’ve taken from R&D to production. What was your role in each stage? Answer: The most complex project I led was a real-time fraud detection system for a financial institution. In the R&D phase, I collaborated with data scientists to select the optimal model architecture and validate its performance. My key role was to introduce a structured approach for experiment tracking using MLflow, ensuring we could reproduce results and audit every model version. For the transition to production, I designed the CI/CD pipeline, treating the model as an immutable artifact packaged in a Docker container. I worked with the DevOps team to set up a Kubernetes cluster for deployment and integrated a monitoring system to track data and model drift in real time. My role was to bridge the data science, DevOps, and security teams, ensuring a smooth and secure path to production. 2. How do you define the MLOps lifecycle? What are the key differences and overlaps with traditional DevOps? Answer: MLOps is a set of practices that automates and standardizes the entire machine learning lifecycle, from data acquisition to model deployment and monitoring. The key overlap with DevOps is the emphasis on automation, collaboration, and continuous delivery. The main difference is the inclusion of three new components: Data, Models, and Experiments. Unlike traditional software, ML models require continuous monitoring for drift, and the pipelines must be triggered by both code changes and data changes. MLOps introduces concepts like data versioning, model registries, and retraining pipelines, which are not part of a standard software development lifecycle. 3. Explain your experience with model lifecycle management, from training and versioning to deployment and monitoring. Answer: I have hands-on experience managing the entire model lifecycle. In my previous role, we used DVC for data versioning and MLflow for experiment tracking, which ensured reproducibility during the training phase. Once a model was ready, it was registered in our model registry. I designed a CI/CD pipeline that automatically packaged the model and its dependencies into a Docker image, ran automated tests, and deployed it to a production environment. We implemented a monitoring system with Prometheus and Grafana to track key metrics like model accuracy, latency, and data drift, with automated alerts to trigger retraining pipelines when performance degraded. 4. Can you walk us through a practical example of how you’ve handled data and model versioning for a large-scale project? Answer: In a predictive maintenance project, we dealt with terabytes of sensor data. Storing it all in Git was not feasible. We used DVC to version the datasets and link them to our Git repository. For models, we leveraged MLflow’s Model Registry. Each model training run was logged with its specific parameters, metrics, and data snapshot ID. This gave us a complete audit trail. If a deployed model showed an issue, we could easily trace it back to the exact version of the data it was trained on and the code that generated it, ensuring full reproducibility. 5. What’s your preferred stack for experiment tracking and why? How have you used it to ensure reproducibility? Answer: My preferred stack for experiment tracking is MLflow. Its open-source nature, broad integrations, and three-component design (Tracking, Projects, and Models) make it highly flexible. I’ve used it to log every experiment, storing parameters, metrics, and artifacts in a centralized server. This allows data scientists to easily compare hundreds of runs, identify the most performant models, and ensure reproducibility. By linking each run to a specific Git commit and data version (via DVC), we can rebuild the exact model and environment at any time. 6. How do you handle model drift and concept drift in a production environment? What tools would you use to monitor for these? Answer: I handle model and concept drift by implementing a continuous monitoring and retraining loop. The first step is to establish baseline performance metrics and data schema from the training data. In production, I would use tools like Evidently AI or a custom solution built with libraries like Great Expectations to compare the incoming production data distribution against the baseline. When a significant drift is detected, an alert is triggered. This alert initiates an automated retraining pipeline, which fetches fresh data, retrains the model, and deploys the new version if it meets the required performance metrics. 7. Discuss your experience with ML tooling platforms like MLflow, Kubeflow, or SageMaker. What are their strengths and weaknesses? Answer: I have experience with all three. MLflow excels at experiment tracking and model management; its primary strength is its simplicity and framework-agnostic nature, making it a great starting point. Its weakness is a lack of native orchestration for complex pipelines. Kubeflow is a powerful, open-source solution for orchestrating end-to-end ML workflows on Kubernetes. Its strength is its flexibility and scalability, but its weakness is its operational complexity and steep learning curve. SageMaker is a comprehensive, fully managed platform. Its strength is its seamless integration with the AWS ecosystem and the ability to abstract away infrastructure management, but it can lead to vendor lock-in and a higher cost. 8. How would you design a CI/CD pipeline for an AI-powered application that needs to be retrained weekly? Answer: I’d design a dual-trigger CI/CD pipeline. The first trigger would be a code change in the Git repository, which would run unit tests and static analysis. The second trigger would be a scheduled weekly job or a data change event (e.g., new data landing in a specific S3 bucket). This trigger would: Pull the latest data and code. Run the training pipeline. Validate the new model’s performance against the existing production model. If the new model performs better, it would be registered and promoted. The pipeline would then build and push a […]
