Technical practice
How would you choose an LLM for a generative AI application?
What to coverLLM proficiency and applying generative AI models are central to this role. Preparation suggestion: Compare candidate models against the application’s quality, latency, cost and deployment constraints, and explain how you would test the choice.
How would you evaluate the quality of an LLM-powered solution?
What to coverThe posting calls for production-ready solutions that meet quality standards. Preparation suggestion: Describe representative test cases, human review where useful, and checks for inaccurate or unsuitable outputs before and after release.
How would you design a production-ready AI application pipeline?
What to coverThe role explicitly covers cloud or on-premises application pipelines. Preparation suggestion: Walk through inputs, model inference, outputs, deployment and failure handling, explaining where you would check quality.
How would you make an AI system scale as demand grows?
What to coverDesigning and implementing scalable AI systems is a stated skill. Preparation suggestion: Discuss throughput, latency and resource use, then explain what you would measure before changing the design.
How would you deploy an AI solution using a cloud platform?
What to coverThe posting asks for experience deploying AI solutions on AWS, Azure or Google Cloud. Preparation suggestion: Use a platform you know to explain deployment steps and trade-offs, without assuming this role uses a particular provider.
How would you troubleshoot a production AI application that has become slow or unreliable?
What to coverProduction-ready quality and problem-solving are part of the advertised work. Preparation suggestion: Explain how you would narrow the issue to the application pipeline, model inference or deployment environment, then verify a fix.
When would you use TensorFlow or PyTorch in an AI solution?
What to coverThe posting names these frameworks as examples of the deep learning knowledge sought. Preparation suggestion: Discuss a relevant model-development task and the trade-offs you would consider when choosing a framework.
What NLP considerations matter when preparing text for an LLM application?
What to coverFamiliarity with NLP techniques and tools is a stated skill. Preparation suggestion: Explain how text quality, segmentation and input length can affect model behaviour, and how you would check the resulting outputs.
How would you assess whether a chatbot meets its intended purpose?
What to coverChatbots are listed as a possible, not guaranteed, project area. Preparation suggestion: Discuss task completion, response quality and failure cases, and explain how findings would guide improvements.
Behavioural practice
Tell me about a consequential technical decision you made with a team.
What to coverThe role includes responsibility for team decisions. Preparation suggestion: Give a real example covering the situation, how you weighed options and involved the team, and the result.
Describe a time you worked across teams to resolve a disagreement.
What to coverThe posting expects collaboration across multiple teams on key decisions. Preparation suggestion: Explain the situation, how you established shared criteria and reached a decision, and what resulted.
Tell me about a time you mentored a junior colleague.
What to coverMentoring junior team members is an explicit responsibility. Preparation suggestion: Describe the skill or problem involved, the support you provided, and the observable result.
Describe a process or workflow you improved.
What to coverThe role calls for continuously evaluating and improving workflows. Preparation suggestion: State the original problem, what you changed and how you assessed the outcome.
Tell me about a difficult problem you solved beyond your immediate team.
What to coverThe posting expects solutions to problems within and across teams. Preparation suggestion: Set out the situation, your specific actions and coordination with others, and the result.