Technical practice
How would you structure a Python application that integrates an LLM?
What to coverThe posting calls for developing and maintaining Python applications and integrating LLM solutions. Suggested preparation: explain how you would separate application logic from model integration, handle failures, and keep the code maintainable.
How would you expose a generative AI feature through a REST API?
What to coverThe role includes APIs and backend services, with REST, JSON, and microservices listed as requirements. Suggested preparation: describe request and response design, input validation, error handling, and the trade-off between a separate service and integration into an existing one.
How would you choose and use an AI/ML library for model integration?
What to coverThe posting names LangChain, Hugging Face, OpenAI APIs, Transformers, and Scikit-learn. Suggested preparation: explain what a relevant library would contribute to a specific integration task, when a simpler direct API call might suffice, and how you would verify its output.
How would you improve a prompt that produces inconsistent answers?
What to coverPrompt engineering and optimizing model outputs are stated responsibilities. Suggested preparation: discuss clarifying instructions, testing representative inputs, and comparing outputs for accuracy and consistency rather than relying on one successful response.
How does retrieval-augmented generation work, and when would you use it?
What to coverThe posting explicitly requires understanding of RAG. Suggested preparation: explain retrieval followed by generation using retrieved context, and discuss the trade-offs of retrieval quality, response relevance, and additional complexity.
What would you consider when adding a vector database to a RAG solution?
What to coverExposure to FAISS, ChromaDB, or Pinecone is preferred, not stated as essential. Suggested preparation: discuss how stored representations support retrieval and how you would assess relevance and operational fit without claiming one product is required.
How would you connect an AI feature to an enterprise application and its database?
What to coverIntegrating AI solutions with enterprise applications and databases is a stated duty; SQL and NoSQL familiarity is listed. Suggested preparation: describe the data flow, how you would keep inputs and outputs consistent with the application, and when a relational or non-relational store fits the data.
How would you investigate a slow or failing Python-backed AI feature?
What to coverThe role includes troubleshooting, debugging, and optimizing application performance. Suggested preparation: explain how you would isolate application, database, and model-call delays, reproduce the issue, and measure whether a change improved it.
How would you use Git while maintaining a Python and AI integration?
What to coverThe posting calls for understanding Git and version control. Suggested preparation: explain how you would track code and prompt changes, review a change before merging it, and make a problematic change easier to identify or revert.
What would you consider when deploying and operating this kind of solution in the cloud?
What to coverBasic Azure, AWS, or GCP knowledge is listed; MLOps and model deployment are preferred topics. Suggested preparation: outline the components you would need to deploy, how you would check that the application and model integration still work, and the trade-off between a simple deployment and added operational controls.
Behavioural practice
Tell me about a time you clarified a technical requirement with people from different functions.
What to coverThe role calls for cross-functional collaboration to understand business requirements. Suggested practice: use a real situation, the questions and actions you took to reach a shared understanding, and the resulting decision or outcome.
Tell me about a time you debugged an application issue.
What to coverTroubleshooting and debugging are stated responsibilities. Suggested practice: describe the situation, how you narrowed down the cause and acted on it, and what the result showed.
Tell me about a time you learned an unfamiliar technology quickly enough to use it.
What to coverThe posting values quick learning of emerging AI technologies. Suggested practice: give a real example of the need, the steps you took to learn and test the technology, and the outcome; it need not be an AI example.
Tell me about a time you balanced speed with quality on a delivery.
What to coverThe posting mentions a fast-paced environment and delivering quality solutions. Suggested practice: describe the time pressure, what you prioritized or checked, and the result, including any trade-off you communicated.