Technical practice
How would you design an agentic AI solution from request to deployment?
What to coverThe posting calls for designing, developing, and deploying agentic AI solutions. Suggested preparation: explain how you would define agent responsibilities, connect them to required systems, and evaluate whether the deployed solution works reliably.
When would you use multiple agents rather than one?
What to coverThe role calls for experience with multi-agent systems. Suggested preparation: discuss task division and coordination, including the trade-off between specialized agents and added complexity.
How would you choose and structure an orchestration framework?
What to coverThe posting names LangChain and CrewAI as examples. Suggested preparation: compare how you would represent agent steps and manage execution in a familiar framework, without assuming either is required for every solution.
How would you improve an LLM prompt that produces inconsistent results?
What to coverThe posting calls for LLM and prompt-engineering experience. Suggested preparation: describe how you would identify failure patterns, revise instructions or context, and test whether the change improves results.
How would you integrate an MCP-based AI solution with another system?
What to coverThe posting specifically mentions MCP-based integrations. Suggested preparation: explain the interface boundaries, what information the AI solution needs to exchange, and how you would diagnose an integration failure.
How would you build a Python-based AI integration with a backend API?
What to coverThe role specifies strong Python skills and exposure to APIs and backend integration. Suggested preparation: walk through request handling, error handling, and how you would test the integration.
What would you consider when connecting an AI solution to a database?
What to coverDatabase integration is stated in the posting. Suggested preparation: discuss how you would retrieve the needed data, handle unexpected or missing results, and limit access to what the solution requires.
How would you approach an AI integration involving Grafana?
What to coverThe posting names Grafana platforms as an integration target but does not specify a use case. Suggested preparation: clarify the intended data flow first, then explain how you would verify that the integration presents or exchanges the expected information.
How would you evaluate a model-optimization change?
What to coverThe role mentions model optimization techniques without prescribing a method. Suggested preparation: define the problem you are optimizing for, compare results before and after a change, and discuss quality versus resource or response-time trade-offs.
How would you troubleshoot an agentic AI solution after deployment?
What to coverThe posting seeks real-world AI deployment experience. Suggested preparation: explain how you would isolate whether a failure comes from the model, agent coordination, or an external integration, then verify the fix.
Behavioural practice
Tell me about a real AI solution you helped deploy.
What to coverThe posting emphasizes real-world AI deployments. Suggested preparation: use a real situation, your specific actions through deployment, and the result, including what you learned from any issues.
Describe a time you solved a complex technical problem.
What to coverThe role calls for solving complex problems. Suggested preparation: describe the situation, how you narrowed down the problem and acted, and the resulting outcome.
Give an example of working effectively when priorities moved quickly.
What to coverThe posting mentions a fast-paced environment. Suggested preparation: explain the situation, how you decided what to address first, and the result of those choices.
Describe a time you proposed a new technical approach.
What to coverThe posting calls for driving innovation in AI solutions. Suggested preparation: use a real situation, explain how you assessed and communicated the approach, and describe the result without overstating its impact.