Technical practice
How would you design a production-grade AI search solution from requirements through deployment?
What to coverThe posting calls for production-grade AI/ML and Generative AI solutions. Suggested preparation: explain how you would define the search task, choose components, validate retrieval and responses, and monitor the deployed system without assuming a particular infrastructure.
How would you design retrieval for a RAG system using embeddings and a vector database?
What to coverThe role explicitly mentions RAG, embeddings, vector databases and information retrieval. Suggested preparation: discuss indexing, retrieval quality, and the trade-offs between finding relevant context and keeping latency manageable.
How would you use search intent and entities to improve results for conversational queries?
What to coverThe posting names search-intent analysis, semantic search, NLP and entity-based optimization. Suggested preparation: explain how you would distinguish user intents, represent relevant entities and check whether retrieved results answer the query.
How would you investigate ranking signals and develop a rank-modelling approach for GEO?
What to coverThe ideal-candidate section emphasizes rank modelling for GEO and analysis of ranking signals. Suggested preparation: describe candidate signals, how you would test their relationship to relevance, and why observed rankings alone do not establish how an external system works.
How would you structure content and data for AI-driven discovery?
What to coverThe posting calls out content structuring for AI consumption, including schema, context building and knowledge representation. Suggested preparation: explain how clear entities, context and relationships could make content easier to retrieve and interpret, and how you would assess the effect.
How would you diagnose a relevant-looking AI answer that uses poor sources or citations?
What to coverThe duties include analyzing AI-generated responses, retrieval patterns and citations. Suggested preparation: trace the query through retrieval and response generation, separate source-selection problems from answer-generation problems, and describe checks for supported claims.
How would you measure whether a GEO or AEO change improved performance?
What to coverThe role requires experiments and evaluation frameworks for GEO/AEO effectiveness and AI search performance. Suggested preparation: define a query set, baseline, relevant outcome measures and a comparison method; address variation in generated answers and the risk of mistaking visibility for answer quality.
How would you evaluate prompt changes for an LLM-based search experience?
What to coverPrompt engineering and exposure to AI/LLM platforms are listed in the posting. Suggested preparation: explain how you would compare prompts on the same queries and retrieved context, examining relevance, groundedness and consistency rather than relying on a few favorable examples.
How would you balance accuracy, relevance, latency and scalability after launch?
What to coverThe posting explicitly asks for monitoring and continuous optimization of these qualities alongside user experience. Suggested preparation: describe what you would measure, how you would isolate a regression, and how a quality improvement might affect response time or capacity.
How would you compare discoverability across conversational and generative search experiences?
What to coverThe role covers GEO, AEO and SGE initiatives across AI-driven search and generative interfaces. Suggested preparation: use comparable queries to inspect which content is retrieved, cited or reflected in answers, while treating platform behavior as something to observe rather than assume.
Behavioural practice
Tell me about a time you translated a loosely defined business need into an AI solution.
What to coverThe posting calls for collaboration with Product, Engineering, Content, SEO, Marketing and Business teams. Suggested preparation: use a real situation, explain how you clarified requirements and aligned stakeholders, then state the result.
Describe a time you led an initiative involving both technical and content teams.
What to coverLeading GEO, AEO and SGE initiatives is part of the advertised work. Suggested preparation: give a real example of the goal, your coordinating actions and the outcome; distinguish your contribution from the team's.
Tell me about a time evidence challenged your initial optimization approach.
What to coverThe role involves experiments and analysis of search responses, retrieval and ranking signals. Suggested preparation: describe the situation, what the evidence showed, how you changed course and what resulted.
Describe a time you handled a trade-off between solution quality and production performance.
What to coverThe posting names accuracy, relevance, latency, scalability and user experience as optimization concerns. Suggested preparation: use a real situation to explain the competing priorities, the decision you made and its measured or observed result.
Tell me about a time you assessed a new AI or search development for practical use.
What to coverThe role asks for keeping current with Generative AI and emerging search technologies. Suggested preparation: describe what prompted the assessment, how you tested its relevance to the work and what decision or learning followed.