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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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HR/EX CDAO Vice President Semantic Architecture & Context Engineering will lead the effort to define and scale the semantic foundation for enterprise data, analytics, AI, and agentic ecosystems. This role involves establishing standards for business concepts, metadata, and retrieval strategies to ensure information is machine-readable and optimized for generative AI consumption. The individual will build reusable patterns for RAG, natural-language-to-data solutions, and automated workflows while leading an experimental discipline using A/B testing and production telemetry to improve accuracy and reliability. Key responsibilities include developing semantic lifecycle governance, conducting context experimentation, and partnering with cross-functional teams to integrate structured knowledge into data products. Required expertise includes semantic architecture, ontology development, and GenAI architectures like vector retrieval and prompt engineering. Preferred skills include Databricks, knowledge graphs, and experience in highly governed environments.
Define the enterprise framework for representing business meaning across data, analytics, AI, and agentic systems.
Create machine-readable standards for business concepts, metrics, metadata, and knowledge structures to ensure semantic interoperability.
Design context architectures for GenAI solutions including system instructions, retrieval strategies, and tool descriptions.
Establish a systematic testing framework using A/B testing and production telemetry to evaluate the accuracy of AI-enabled solutions.
Develop lifecycle practices for the creation, versioning, and governance of semantic assets across the enterprise.
Use AI-assisted techniques to automate metadata generation, concept extraction, and taxonomy development.
Create playbooks, templates, and training materials to enable cross-functional teams to adopt semantic best practices.
What we're looking for
Significant experience in semantic architecture, data architecture, knowledge engineering, ontology development, data product management, AI/ML engineering, information architecture, or advanced analytics.
Strong understanding of enterprise data architecture including logical and physical models, metadata, metrics, dimensions, lineage, and governance.
Demonstrated understanding of modern GenAI architectures, including LLMs, RAG, vector retrieval, and agentic systems.
Experience translating complex business concepts and domain knowledge into structured technical representations.
Strong analytical orientation with experience designing experiments or quantitative methods to assess solution performance.
Ability to operate across business, product, data, engineering, and architecture organizations while influencing without direct ownership.
Experience with Databricks technologies including Unity Catalog, Delta Lake, MLflow, Vector Search, and Genie (preferred).
Familiarity with knowledge graphs, semantic technologies (RDF, OWL, SPARQL), or AI evaluation frameworks (preferred).
Information Architecture
Data Management
NoSQL
ETL
Data Lakes
Data Governance
Multi-cloud
AI
Site Reliability Engineering
IT Service Management
Configuration Management