Enterprise Semantic Architecture Lead for AI & Data

Next Frontier Capital; New York (NY); On-site; USD 250,000 - 350,000; Full time

Benefits offered by this job

Health care coverage

On-site wellness centers

Retirement savings plan

Backup childcare

Tuition reimbursement

Job summary

JPMorgan Chase & Co. seeks a leader for Semantic Architecture & Context Engineering to define and scale the semantic foundation of our enterprise data, analytics, AI, and agentic ecosystem.

You will drive standards for semantic models, metadata, and knowledge structures across data products and GenAI solutions. You will collaborate with Data, Analytics, AI, Product, Engineering, and business teams to implement an experimental discipline around context, using evaluations, telemetry, and

Qualifications

• 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 data models, metadata, data products, metrics, dimensions, lineage, and data governance.
• Demonstrated understanding of modern GenAI architectures, including LLMs, RAG, vector retrieval, natural-language-to-data solutions, prompt/context engineering, and agentic architectures.
• Experience translating complex business concepts and domain knowledge into structured technical representations.
• Strong analytical orientation with experience designing experiments, evaluations, or other quantitative methods for assessing solution performance.
• Ability to operate effectively across business, product, data, engineering, and architecture organizations.
• Strong written and verbal communication skills and the ability to influence without direct ownership of participating teams.

Responsibilities

• 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 representations.
• Define principles for semantic interoperability across data products and domains.
• Establish semantic and context architecture patterns that can be applied across structured, semi-structured, and unstructured information.
• Define best practices for structuring the context provided to GenAI and agentic solutions.
• 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 Databricks Genie spaces/rooms, enterprise AI assistants, analytical agents, and domain-specific agents.
• Ensure context structures establish authoritative definitions, permitted sources, relationships, hierarchies, temporal logic, calculation rules, and other constraints for reliable AI reasoning.
• Help establish standards for context isolation, inheritance, reuse, versioning, and lifecycle management across an expanding portfolio of AI solutions.
• Identify opportunities to embed AI throughout the semantic lifecycle and accelerate metadata generation, concept extraction, taxonomy development, and quality assurance.
• Partner with Data Product, Data Engineering, AI/ML, BI, Product, Design, Architecture, and business teams to embed semantic best practices into delivery.
• Develop playbooks, templates, education, and reusable implementation patterns for semantic adoption.

Skills

Semantic architecture

Data architecture

Knowledge engineering

Ontology development

GenAI engineering

RAG

AI/ML engineering

Communication

Tools

Databricks Unity Catalog

Delta Lake

Databricks SQL

MLflow

Genie

Job description

JPMorgan Chase & Co. seeks a leader for Semantic Architecture & Context Engineering to define and scale the semantic foundation of our enterprise data, analytics, AI, and agentic ecosystem.

You will drive standards for semantic models, metadata, and knowledge structures across data products and GenAI solutions. You will collaborate with Data, Analytics, AI, Product, Engineering, and business teams to implement an experimental discipline around context, using evaluations, telemetry, and