Technical practice
How would you design an AI agent that can reason, plan, and execute a workflow?
What to coverSuggested preparation: Explain how you would represent the task, choose actions, manage state, and determine when the workflow is complete. Discuss how you would handle failed actions and limit unintended execution.
How would you build an end-to-end AI proof of concept across frontend, backend, and orchestration layers?
What to coverSuggested preparation: Trace a user request through the interface, APIs, orchestration, and response. Explain what you would build first to demonstrate the idea and what you would revisit before making it a maintained feature.
How would you integrate an NLP model, LLM, or reasoning engine into a user-facing feature?
What to coverSuggested preparation: Cover the backend integration and how its outputs would reach the user-facing component. Discuss how you would evaluate output usefulness and handle errors or uncertain results.
How would you divide an AI feature between a modern JavaScript frontend and Node.js or Python services?
What to coverSuggested preparation: Explain where interface logic, API handling, and model integration belong. Discuss type contracts and the trade-offs of using more than one language across the feature.
When would you choose REST or GraphQL for an API serving an AI-driven feature?
What to coverSuggested preparation: Compare their fit for the data a frontend needs, including response shape and API evolution. Show how you would make model-backed operations understandable to API consumers.
How would you choose among search, relational, document, and cache data stores for a feature?
What to coverSuggested preparation: Use the posting’s examples—Elasticsearch or Solr, MySQL or PostgreSQL, MongoDB, and Redis—to discuss query needs, consistency, and performance. Explain the workload that would justify each choice rather than assuming every feature needs every store.
What would you consider when connecting an agentic workflow to an MCP-based integration?
What to coverSuggested preparation: The posting asks for familiarity with MCPs; explain how you would assess the integration’s capabilities, inputs, outputs, and failure behavior. Describe how you would constrain access to actions or data before allowing an agent to use it.
How would you test an AI feature at unit, integration, and API levels?
What to coverSuggested preparation: Give a distinct purpose for each level, from deterministic workflow logic to service boundaries and API contracts. Explain how you would test failure cases without relying solely on variable model outputs.
How would you evaluate the performance and scalability of an agentic AI feature?
What to coverSuggested preparation: Discuss response time, concurrent requests, and the cost of multiple model or service calls. Explain what you would measure before choosing between optimizations that might affect answer quality.
How would you troubleshoot a production failure spanning the frontend, API, and AI integration?
What to coverSuggested preparation: Describe how you would isolate the failing layer using the request path and available error evidence. Cover safe mitigation, verification of the fix, and a test that could prevent recurrence.
Behavioural practice
Tell me about a time you turned cross-functional requirements into tested code.
What to coverSuggested preparation: Use a real situation involving product, ML, design, or comparable partners; state your responsibility and the actions you took to resolve ambiguity. Finish with the delivered result and how you verified it met the agreed requirements.
Describe a time sprint priorities changed while you were delivering a feature.
What to coverSuggested preparation: Explain the situation, how you raised trade-offs during planning or another team discussion, and what you did next. Give the result for delivery or team coordination.
Tell me about a production issue you helped resolve.
What to coverSuggested preparation: Set out the user or system impact, your specific troubleshooting and communication actions, and the outcome. Distinguish what you knew at the time from what you learned afterward.
Describe a time you identified a quality or information-security concern.
What to coverSuggested preparation: Choose a real example and explain the situation, the procedures or reporting path you followed, and your own actions. State the result without disclosing sensitive details or assuming a particular company policy.
Tell me about a time you evaluated an emerging AI or engineering tool for practical use.
What to coverSuggested preparation: Describe the need, how you assessed whether the tool was useful, and the trade-offs you communicated. End with the decision or result, even if the team chose not to adopt it.