Job DescriptionThe RoleThe Vice President, AI Knowledge Engineering will lead the design and delivery of the knowledge substrate on which every AI product in the enterprise depends - the ontologies that define our entities, the graph that connects them, the metadata that makes them discoverable, and the interfaces that make them safely accessible to agents. This is a build-and-transform mandate within the office of the SVP, AI & Engineering, with full ownership of the architecture and a multi-year horizon to get it right.
Your Day-to-Day- Enterprise Ontology & Semantic Layer: Define and govern the shared vocabulary of the enterprise - so every system, every model, and every agent shares one definition of guest, property, stay, and transaction. This is the foundational artefact of knowledge engineering.
- Connected Knowledge Graph: Move from rows-and-tables to a relationship-first intelligence layer that links guest signals, property attributes, loyalty behavior, and operational events into a traversable graph that AI agents can reason over.
- Agent-Discoverable Metadata: Tag the data estate with machine-readable ontologies, lineage, freshness indicators, and access classifications so AI systems can self-discover and trust enterprise data without human intermediation.
- MCP Servers & Agent APIs: Stand up the Model Context Protocol layer and governed APIs through which internal and partnered AI agents query knowledge, trigger actions, and operate with full audit and policy control.
- Real-Time Knowledge Movement: Replace batch dependencies with event-driven pipelines so the knowledge graph and every downstream AI consumer operate on current reality, not yesterday's snapshot.
What We Need from You- Twelve or more years in knowledge engineering, enterprise data, or applied-AI platform leadership, with at least five years owning end-to-end delivery at scale.
- Demonstrable experience designing and operating one or more of: enterprise ontologies, semantic layers, production knowledge graphs, or real-time data infrastructure - in a global or hyperscale operating environment.
- Working fluency with the agentic-AI stack: model context interfaces, retrieval architectures, vector and graph stores, and the governance patterns that make them safe at enterprise scale.
- Track record of leading large engineering and data organizations, including hiring, levelling, and developing senior technical talent.
- Comfort operating with executive stakeholders - board, audit committee, regulators, owners, and franchise partners - on data, privacy, and AI risk.
- The role owns five interconnected capabilities, delivered sequentially in year one and operated in parallel thereafter.
Preferred Experience- Public-company exposure: comfortable with disclosure discipline, segment reporting implications, and the cadence of investor communication.
- Background in hospitality, travel, retail, or another consumer-scale industry where customer identity and real-time operational signals are core to competitive advantage.
- Experience leading a transition from legacy batch and warehouse models toward streaming, graph, and agent-accessible architectures.
- Direct experience designing or contributing to industry-level data standards, partnerships with hyperscalers, or external developer ecosystems.
Location - Atlanta, GA, preferred. Our hybrid work structure is an expectation of three (3) days a week in office. This expectation may be adjusted to evolve with the changing needs of the business.
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