Technical practice
How would you select AWS AI Services for a production AI application?
What to coverAWS AI Services proficiency is a must-have skill in the posting. Suggested preparation: explain how you would compare service capabilities, integration needs, cost, and operational constraints against the application’s requirements.
How would you design an AI application pipeline for cloud or on-premises deployment?
What to coverThe role calls for production-ready applications with a cloud or on-premises pipeline and a strong understanding of cloud architecture. Suggested preparation: describe the pipeline’s components, deployment boundaries, and trade-offs affecting scalability and reliability.
How do preprocessing and feature engineering choices affect a production model?
What to coverThe posting specifies familiarity with data preprocessing and feature engineering. Suggested preparation: discuss data quality checks, consistent transformations between training and inference, and how you would detect a harmful change in input data.
How would you move a machine learning model from development into production?
What to coverProduction model development and deployment are stated requirements; MLOps, GitLab CI/CD, and infrastructure as code are preferred skills. Suggested preparation: walk through validation, versioning, release, and rollback, distinguishing practices you have used from options you would propose.
When would you use retrieval-augmented generation in a generative AI solution?
What to coverThe role may incorporate generative AI models, while LLMs and retrieval-augmented generation are preferred skills. Suggested preparation: explain when retrieval helps, how you would assess answer quality, and the trade-offs in latency and system complexity.
How would you integrate an AI agent into an application workflow?
What to coverAI agents and workflow integration appear among the preferred skills. Suggested preparation: outline how you would define permitted actions, handle failures, and evaluate whether an agent is preferable to a simpler workflow.
How would you evaluate a deep-learning approach to image processing?
What to coverDeep learning, neural networks, and image processing are possible parts of the advertised solutions. Suggested preparation: discuss dataset quality, evaluation measures suited to the task, and the trade-off between model performance and inference demands.
How would you assess whether a chatbot is ready for production?
What to coverThe posting identifies chatbots as a possible application and emphasizes production-ready quality. Suggested preparation: cover task-specific answer evaluation, failure handling, response time, and how users would be supported when the chatbot cannot answer.
What would you monitor after deploying an AI system?
What to coverThe role emphasizes reliability; Prometheus, Grafana, Elastic Stack, data observability, and SRE observability are preferred skills. Suggested preparation: distinguish application health, data quality, and model-output signals, then explain how an alert would lead to investigation.
How would you diagnose a production AI system that has become slow or unreliable?
What to coverThe posting calls for solutions optimized for performance and reliability. Suggested preparation: describe how you would isolate problems in data preparation, model inference, cloud services, and application dependencies before choosing a fix.
Behavioural practice
Tell me about a consequential technical decision you made for a team.
What to coverThe role includes responsibility for team decisions and subject-matter leadership. Suggested preparation: use a real situation, explain the options and how you made the decision, then state the result and what you learned.
Describe a time you aligned multiple teams on an AI or engineering decision.
What to coverThe posting expects collaboration across teams on key decisions. Suggested preparation: give a real situation, the differing priorities, your actions to reach alignment, and the resulting decision or outcome.
Tell me about a time you mentored colleagues or shared technical knowledge.
What to coverMentorship and knowledge sharing are explicit responsibilities. Suggested preparation: describe a real skill gap or problem, what you did to help others, and the observable result for the team.
Describe a problem you solved that affected more than your immediate team.
What to coverThe role calls for solutions to problems within and across teams. Suggested preparation: explain the situation and its wider impact, how you coordinated the response, and what changed as a result.
Tell me about a time you evaluated and introduced an AI or ML best practice.
What to coverEvaluating and implementing best practices to improve project outcomes is a stated responsibility. Suggested preparation: describe the original problem, the evidence and trade-offs behind your choice, how you introduced the change, and its result.