style="font-size: 10pt; line-height: 107%;">The
Role
The 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
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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.
style="font-family: Arial, sans-serif;">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.
style="font-family: Arial, sans-serif;">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
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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.
style="font-family: Arial, sans-serif;">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.
style="font-family: Arial, sans-serif;">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.
style="font-size: 10pt; line-height: 107%;">The role owns five interconnected
capabilities, delivered sequentially in year one and operated in parallel
thereafter.
style="color: rgb(237, 125, 49); font-family: Arial, sans-serif;">
style="font-size: 10pt; line-height: 107%;">Preferred
Experience
-
style="font-size: 10pt; line-height: 107%;">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. -
style="font-family: Arial, sans-serif;">Direct experience designing or contributing
to industry-level data standards, partnerships with hyperscalers, or external
developer ecosystems.
style="font-family: Arial, sans-serif;">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.
#LI-PF1
