HR/EX CDAO Vice President Semantic Architecture & Context Engineering

We are seeking a Semantic Architecture & Context Engineering Lead to help define and scale the semantic foundation underpinning our enterprise data, analytics, AI, and agentic ecosystem.

As organizations increasingly consume data through natural-language interfaces, generative AI, intelligent agents, predictive models, and automated workflows, the quality of the underlying context becomes as important as the quality of the underlying data.

Business concepts, metrics, relationships, definitions, metadata, instructions, retrieval strategies, and decision rules must be structured in ways that are consistent, machine-readable, reusable, governed, and optimized for AI consumption.

This role will establish the practices, standards, and reusable patterns through which business and data context is represented across our data products, analytical models, GenAI solutions, and agent architectures.
The individual will work horizontally across Data, Analytics, AI, Product, Engineering, Architecture, and business domain teams.

They will partner closely with functional owners-including data product managers, AI engineers, data scientists, architects, and business subject-matter experts-to ensure semantic and contextual structures are designed consistently while remaining appropriate to individual domains and use cases.

Critically, this is not solely a governance or documentation role.

The Semantic Architecture & Context Engineering Lead will establish an experimental discipline around context, using structured evaluation, A/B testing, production telemetry, and AI-assisted techniques to continuously improve the accuracy, precision, reliability, performance, and usability of AI-enabled solutions.

Job Responsibilities



* Enterprise Semantic & Context Architecture.

Define the enterprise framework for representing business meaning and context across data, analytics, AI, and agentic solutions.

Establish standards and reusable patterns for semantic models, business concepts, metrics, entities, relationships, taxonomies, metadata, definitions, contextual instructions, and knowledge structures.

Develop a common approach for translating human business concepts into machine-readable and AI-consumable representations.

Define principles for semantic interoperability across data products and domains, minimizing conflicting definitions, redundant logic, and fragmented representations of common enterprise concepts.

Establish semantic and context architecture patterns that can be applied across structured, semi-structured, and unstructured information.


* AI & Agent Context Engineering - Define best practices for structuring the context provided to GenAI and agentic solutions, including system instructions, business rules, semantic metadata, examples, retrieval context, tool descriptions, entity relationships, and domain knowledge.

Partner with AI engineering and architecture teams to design context patterns for RAG, natural-language-to-data, reasoning, agent tool ...