Technical practice
How would you choose between an ML model, an NLP approach, and an LLM-based solution?
What to coverThe role leads development of ML, NLP, and LLM/GenAI solutions. Suggested preparation (inferred): explain how the task, available data, evaluation criteria, latency, and operating cost would shape your choice.
How would you design a maintainable Python-based ML pipeline?
What to coverThe posting calls for developing and maintaining Python-based ML pipelines. Suggested preparation (inferred): discuss data validation, reproducible runs, testing, and how you would diagnose a failed pipeline stage.
How would you manage the database associated with an ML pipeline?
What to coverManaging associated databases is an explicit responsibility; no database product is specified. Suggested preparation (inferred): address data access, consistency, performance, and how you would trace a data-quality problem back to its source.
How would you evaluate an ML model before deployment?
What to coverThe role includes developing and deploying ML solutions. Suggested preparation (inferred): describe a suitable baseline, validation approach, metrics tied to the task, and checks for overfitting or data leakage.
How would you diagnose an underperforming NLP solution?
What to coverNLP solution development and optimization are stated responsibilities. Suggested preparation (inferred): explain how you would inspect input data and error patterns, then compare changes against an appropriate evaluation set.
How would you assess whether a generative AI solution is ready to use?
What to coverThe lead is expected to develop and optimize LLM/GenAI solutions. Suggested preparation (inferred): discuss task-specific quality checks, failure analysis, and trade-offs among output quality, latency, and cost without assuming a particular application.
How would you deploy an AI model or Python application on Azure or AWS?
What to coverThe posting asks for cloud deployment experience and assigns deployment of AI models on Azure or AWS. Suggested preparation (inferred): walk through packaging, configuration, rollout, monitoring, and a rollback plan without assuming a required cloud service.
How would you investigate poor performance or scalability after deployment?
What to coverEnsuring deployment performance and scalability is an explicit responsibility. Suggested preparation (inferred): describe how you would measure the bottleneck, distinguish application, model, and database causes, and test the effect of an optimization.
What practices would you use to manage an LLM solution throughout its lifecycle?
What to coverThe role includes implementing LLMOps best practices for efficient model lifecycle management. Suggested preparation (inferred): cover versioning, evaluation before changes, monitoring after release, and how to compare or reverse changes when quality declines.
Behavioural practice
Tell me about a time you led an ML or NLP solution from development through deployment.
What to coverLeading development and deployment is central to this role. Suggested preparation (inferred): give the situation, the decisions and actions you owned, and the measurable result; distinguish your contribution from the team's.
Describe a time you mentored someone on a technical problem.
What to coverMentoring team members is a stated responsibility. Suggested preparation (inferred): explain the person's challenge, how you adapted your guidance, and what changed as a result.
Tell me about a time you evaluated a new ML, NLP, or GenAI development for practical use.
What to coverThe posting asks the lead to stay current with advances in these areas. Suggested preparation (inferred): describe the situation, how you tested the idea against an existing approach, and the outcome of your recommendation.
Describe a time you had to resolve a performance or scalability issue in a deployed solution.
What to coverThe role emphasizes deployment, optimization, performance, and scalability. Suggested preparation (inferred): recount the situation, your diagnostic and prioritization actions, and the observed result, including any trade-off you accepted.