Technical practice
How would you choose and develop an AI/ML model for an enterprise use case?
What to coverSuggested preparation: Explain how you would define the business objective, select evaluation measures, and compare a simpler approach with a deep-learning approach. Discuss how Python and a relevant framework such as Scikit-learn, PyTorch, or TensorFlow would support development and testing.
How would you design a reliable ETL pipeline for a production AI application?
What to coverSuggested preparation: Cover data validation, transformations, failure handling, and maintaining consistent inputs for training and inference. Explain how you would troubleshoot a data-quality issue without assuming a particular data platform.
How would you structure an LLM-based application using a modern framework?
What to coverSuggested preparation: Describe the roles of the model, application logic, and a framework such as LangChain, LlamaIndex, or Hugging Face. Explain when a framework helps and when simpler integration may be easier to maintain.
How would you improve and evaluate prompts for an LLM application?
What to coverSuggested preparation: Discuss creating representative test cases, comparing outputs against the task's requirements, and checking for inconsistent or incorrect responses. Explain the trade-off between output quality, latency, and cost when revising prompts.
How would you deploy a model through an API and integrate it with an enterprise application?
What to coverSuggested preparation: Explain request and response contracts, input validation, error handling, and versioning. Cover how you would test the integration and manage changes without disrupting the consuming application.
How would you use MLOps practices to move a model from development to production?
What to coverSuggested preparation: Describe reproducible runs, tracked model versions, pipeline orchestration, and deployment checks. Relate relevant experience with MLflow, Kubeflow, or Airflow to those functions rather than treating the tools as interchangeable.
What would you monitor after deploying an AI or GenAI solution?
What to coverSuggested preparation: Cover service health, input-data changes, output quality, and measures tied to the intended business outcome. Explain how you would investigate a regression and decide whether to adjust the pipeline, prompt, model, or deployment.
When would you use retrieval-augmented generation and a vector database?
What to coverSuggested preparation: This is a preferred, not required, area in the posting. Explain retrieval and grounding, how you would evaluate retrieved content and final answers, and the added complexity compared with an LLM application without retrieval.
How would you design a scalable cloud deployment for an AI service?
What to coverSuggested preparation: Discuss how compute demand, API traffic, reliability, and operational cost affect a design on Azure, AWS, or GCP. If discussing microservices, identify a concrete reason to separate components rather than assuming that architecture is always preferable.
How would streaming data change the design of an AI data pipeline?
What to coverSuggested preparation: Streaming pipelines are listed as nice to have. Contrast them with batch ETL in terms of latency, late or duplicate data, and recovery, and explain when the added operational complexity would be justified.
Behavioural practice
Tell me about a time you led an AI engineering effort through a difficult technical decision.
What to coverSuggested preparation: Use a real situation, your leadership actions, the alternatives considered, and the result. Make clear how you involved others and reached a decision rather than describing only your individual implementation.
Describe a time you worked across teams to turn a business need into an AI solution.
What to coverSuggested preparation: Set out the situation, how you clarified requirements and communicated trade-offs, and what you did with feedback. State the result in terms the stakeholders could assess, without overstating the solution's impact.
Tell me about a production AI or data-pipeline problem you owned end to end.
What to coverSuggested preparation: Give the situation, your actions from diagnosis through resolution, and the result. Explain how monitoring or subsequent changes reduced the risk of recurrence.
Describe a time you explained a complex AI engineering issue to a non-specialist audience.
What to coverSuggested preparation: Identify the audience and decision at stake, then describe how you adapted your technical communication and checked understanding. Close with the outcome and what, if anything, you would explain differently next time.
Tell me about a time you balanced delivery speed with the quality of an AI solution.
What to coverSuggested preparation: Describe the situation, the competing priorities, and the actions you took to evaluate risks or negotiate scope. Give the result, including any follow-up work needed after delivery.