HR/EX CDAO Vice President Semantic Architecture & Context Engineering

JPMorgan Chase; New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

JPMorgan Chase is seeking a Semantic Architecture & Context Engineering Lead to define and scale the semantic foundation across data, analytics, AI, and agentic ecosystems. You will establish standards, reusable patterns, and governance for machine-readable business context that informs GenAI solutions and autonomous workflows.

You will collaborate with data product managers, AI engineers, architects, and domain experts to ensure context structures are accurate, consistent, and

Responsibilities

• Define the enterprise framework for representing business meaning and context across data, analytics, AI, and agentic solutions.
• Establish standards for semantic models, metadata, and knowledge representations to be reused across domains.
• Lead experiments and governance practices to improve context accuracy, reliability, and AI usability.

Job description

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 use, memory, workflow orchestration, and agent-to-agent interaction. Develop reusable context architectures for solutions such as Databricks Genie spaces/rooms, enterprise AI assistants, analytical agents, and domain-specific agents. Ensure context structures clearly establish authoritative definitions, permitted sources, relationships, hierarchies, temporal logic, calculation rules, and other constraints required for reliable AI reasoning. Help establish standards for context isolation, inheritance, reuse, versioning, and lifecycle management across an expanding portfolio of AI solutions.

Data Product Semantics

Partner with data product managers and domain teams to strengthen the semantic layer of enterprise data products. Establish standards for business definitions, logical models, physical-to-logical mappings, metrics, dimensions, entity relationships, metadata, lineage, data-quality expectations, and consumption guidance. Ensure data products contain sufficient semantic context for use by human analysts, BI platforms, machine-learning models, GenAI applications, and autonomous agents. Promote reusable semantic contracts that allow downstream solutions to interpret data consistently without recreating business logic independently. Identify common concepts and relationships that should be standardized across multiple data domains while preserving appropriate domain ownership.

Context Experimentation & Evaluation

Establish a systematic testing framework for semantic and contextual design. Design and lead experiments to evaluate alternative context structures, instructions, examples, semantic representations, retrieval strategies, and metadata configurations. Use A/B testing, offline evaluation, golden question sets, adversarial testing, production telemetry, and other methods to measure changes in: - answer accuracy, precision and recall, grounding and source fidelity etc

Semantic Lifecycle & Governance

Establish lifecycle practices for creating, reviewing, approving, vers