Technical practice
Designing Enterprise Ontologies for Business Context
What to coverExplain your approach to designing enterprise-level ontologies using OWL and RDF. Discuss how you balance the complexity of upper-level ontologies with the need for practical, domain-specific application in SAP environments.
Entity Resolution and Data Harmonization
What to coverDescribe your methodology for performing entity resolution across disparate systems like SAP, Salesforce, and Workday. Focus on how you handle identity stitching and deduplication to create a unified semantic layer.
Trade-offs: RDF Triple Stores vs. Labeled Property Graphs
What to coverCompare the strengths and weaknesses of RDF triple stores versus labeled property graphs (e.g., Cypher-based) for enterprise knowledge representation. Provide criteria for choosing one over the other in a high-scale AI context.
Operationalizing RAG for Enterprise Data
What to coverDiscuss the challenges of implementing Retrieval-Augmented Generation (RAG) on structured enterprise data. Address how you ensure grounding accuracy and manage the integration of vector databases with existing business process semantics.
Scaling AI Solutions in Production
What to coverDetail your process for taking an AI model from experimentation to a production-ready, scalable service. Include how you handle lifecycle support, monitoring, and continuous improvement in a hybrid cloud environment.
Leveraging SAP Data Models and APIs
What to coverExplain how you would utilize SAP One Domain Model and SAP Graph API to ground AI agents in real-world business processes. Suggestion: Focus on how these tools map to underlying business objects to ensure semantic consistency.
Agentic AI and Orchestration Frameworks
What to coverDescribe your experience with agentic AI, specifically regarding planning, orchestration, and tool use. Explain how you design multi-agent architectures that can reliably execute complex business tasks.
Data Mesh and Semantic Contracts
What to coverHow do you apply data mesh principles to enterprise AI? Discuss the role of semantic contracts in ensuring governed, self-service data consumption across cross-functional teams.
Behavioural practice
Translating Ambiguous Business Challenges
What to coverDescribe a time you had to translate an abstract or ambiguous business problem into a concrete AI use case. Explain your process for defining the scope, technical design, and measurable business outcomes.
Cross-Functional Stakeholder Management
What to coverGive an example of how you have collaborated with non-technical stakeholders, such as product or business teams, to deliver a production-ready AI solution. How did you ensure the solution met their practical needs?
Navigating Technical Trade-offs
What to coverShare an experience where you had to make a difficult technical trade-off between model performance and system scalability. How did you evaluate the options and what was the final impact on the project?
Leading Through Technical Influence
What to coverDescribe a situation where you had to influence a team or organization to adopt a new AI technique or semantic standard. How did you build consensus and address technical skepticism?