Technical practice
How would you architect an AI/ML solution from data ingestion through deployment?
What to coverThe role explicitly covers end-to-end architecture and systems aligned with business goals. Suggested practice: explain component boundaries, data flow, scalability decisions, and how you would test whether the architecture serves the stated goal.
How would you detect and address poor data quality in a large-scale ML pipeline?
What to coverThe posting emphasizes data engineering, pipelines, and data quality. Suggested practice: discuss ingestion checks, dataset validation, ownership of fixes, and the trade-off between blocking bad data and maintaining pipeline availability.
How would you choose a modeling approach and Python framework for a new problem?
What to coverThe role names Python, TensorFlow, PyTorch, and Scikit-learn, alongside deep learning and several application areas. Suggested practice: compare approaches based on the problem, available data, performance needs, and operational complexity rather than assuming one framework is always best.
What would you require before approving a trained model for deployment?
What to coverThe architect is expected to lead training, validation, and deployment strategies while ensuring model performance. Suggested practice: cover appropriate baselines, validation data, relevant metrics, failure analysis, and the trade-off between measured gains and deployment risk.
How would you design CI/CD for a scalable ML pipeline?
What to coverThe posting specifically calls for MLOps pipelines and lists MLflow, Kubeflow, Airflow, Docker, and Kubernetes. Suggested practice: explain what you would version, test, automate, and promote between stages, distinguishing tool choices from the pipeline principles they serve.
How would you investigate a deployed model whose performance has declined?
What to coverMonitoring and retraining are stated responsibilities. Suggested practice: trace the issue through input quality, data or concept drift, service behavior, and evaluation results, then explain when retraining, rollback, or a data fix would be appropriate.
How would you evaluate AWS SageMaker, Azure ML, or GCP AI for an AI solution?
What to coverThe role includes evaluating cloud-based AI solutions and names these platforms. Suggested practice: compare fit with the architecture, integration, scalability, security, and operating effort without assuming the employer has selected a platform.
How would you expose a model through APIs or microservices at scale?
What to coverThe posting calls for knowledge of APIs, microservices, and scalable system design. Suggested practice: address service boundaries, latency and throughput, failure handling, and the trade-off between a separate model service and a simpler deployment.
How would you incorporate security, governance, and explainability into an ML architecture?
What to coverSecurity and compliance are responsibilities; data governance, model explainability, and AI ethics are preferred knowledge areas. Suggested practice: describe access and data-handling controls, model traceability, and how explanation needs could affect model and deployment choices.
When would you use an LLM or generative AI rather than another ML approach?
What to coverLLMs, generative AI, and prompt engineering are preferred qualifications, not a specified project. Suggested practice: compare task fit, validation methods, reliability, cost, and risks; explain how you would evaluate prompts before adopting an approach.
Behavioural practice
Tell me about a time you aligned an AI/ML architecture with a business goal.
What to coverThe role calls for robust, scalable systems aligned with business goals. Suggested practice: use a real situation, the decisions and stakeholder discussions you led, and the resulting outcome or lesson.
Describe a time you guided a team through a difficult technical decision.
What to coverTechnical leadership, mentorship, and architectural guidance are stated responsibilities. Suggested practice: explain the situation, how you helped others weigh alternatives, and the result without claiming sole credit for the team's work.
Give an example of resolving conflicting priorities across data science, engineering, and product.
What to coverThe posting calls for collaboration with these groups and other stakeholders. Suggested practice: describe the competing priorities, the action you took to reach a decision, and its effect on delivery or working relationships.
Tell me about a production ML problem you had to diagnose with others.
What to coverThe role includes model performance, monitoring, and problem-solving. Suggested practice: set out the situation, your investigation and communication, and the result, including what remained uncertain if the cause was not fully established.
Describe a time you evaluated a new AI/ML tool or research idea for practical use.
What to coverThe architect is expected to stay current with AI/ML tools and research. Suggested practice: explain the situation, how you tested usefulness against existing options, and the outcome, including a decision not to adopt it if that was warranted.