Technical practice
Designing Ontology-Driven AI Architectures
What to coverExplain how you would integrate knowledge graphs with generative AI to improve grounding and reasoning in enterprise applications. Discuss the trade-offs between using a rigid, formal ontology versus a more flexible, schema-on-read approach for large-scale business data.
Scaling Multi-Agent Orchestration
What to coverDescribe your approach to managing complex, multi-agent workflows, specifically regarding task decomposition, error handling, and state management. Address how you ensure context durability and consistent communication between autonomous agents in a production environment.
Semantic Retrieval and RAG Optimization
What to coverHow do you optimize RAG pipelines for high-stakes business processes? Discuss the integration of hybrid search systems—combining vector embeddings with knowledge graph traversal—to ensure accuracy and reduce hallucinations.
Enterprise AI Model Governance
What to coverWhat framework would you implement to manage the lifecycle of AI models, including versioning, monitoring, and compliance? Explain how you balance innovation with the need for responsible AI practices and rigorous model validation.
Building Semantic Layers for ERP Data
What to coverGiven the complexity of SAP's business data models, how would you design a semantic layer that translates raw ERP data into a knowledge graph? Focus on the challenges of entity resolution and maintaining taxonomy consistency across disparate business units.
Pragmatic Build-versus-Buy Decisions
What to coverWhen evaluating AI platform investments, what criteria do you use to decide between building custom infrastructure versus leveraging existing enterprise tools like SAP Datasphere or HANA Cloud? Provide an example of how you have aligned such technical decisions with long-term product roadmaps.
MLOps and LLM Ops at Scale
What to coverDescribe the operational rigor required to maintain AI agents in production. Focus on how you handle data drift, model retraining, and the specific challenges of monitoring LLM-based systems compared to traditional machine learning models.
Data Engineering for AI Readiness
What to coverHow do you ensure that data infrastructure supports the high-quality, contextual requirements of AI agents? Discuss the role of data engineering in preparing, cleaning, and structuring enterprise data for consumption by knowledge graphs and semantic layers.
Behavioural practice
Leading Through Complex AI Initiatives
What to coverDescribe a time you led a complex AI initiative from concept to production. Focus on how you managed cross-functional stakeholders, aligned technical strategy with business objectives, and overcame significant technical or organizational hurdles.
Managing Globally Distributed Teams
What to coverProvide an example of how you have fostered a culture of technical excellence and continuous learning within a globally distributed team. How did you ensure alignment and maintain high performance across different time zones and cultures?
Influencing Senior Leadership
What to coverTell me about a time you had to translate a complex technical AI concept into business value for senior executives. How did you secure buy-in for a strategic investment or a change in technical direction?
Hiring and Retaining Top AI Talent
What to coverDescribe your approach to building and scaling a high-performing data science team. Share a specific situation where you had to coach a team member through a difficult challenge or manage a significant change in team priorities.