AI Knowledge Intelligence Foundations Architect, VP

Mitsubishi UFJ Financial Group•Jersey City, NJ

10d•$149,000 - $180,000•Hybrid

About The Position

Do you want your voice heard and your actions to count? Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), one of the world’s leading financial groups. Across the globe, we’re 150,000 colleagues, striving to make a difference for every client, organization, and community we serve. We stand for our values, building long-term relationships, serving society, and fostering shared and sustainable growth for a better world. With a vision to be the world’s most trusted financial group, it’s part of our culture to put people first, listen to new and diverse ideas and collaborate toward greater innovation, speed and agility. This means investing in talent, technologies, and tools that empower you to own your career. Join MUFG, where being inspired is expected and making a meaningful impact is rewarded. The selected colleague will work at an MUFG office or client sites four days per week and work remotely one day. A member of our recruitment team will provide more details. Job Summary: The AI Knowledge Intelligence Foundations Architect is responsible for designing the semantic, lineage, and trust frameworks that enable responsible, explainable, and enterprise-ready AI. This includes building and governing ontologies, semantic models, AI metadata standards, and knowledge graph structures that serve both analytical and generative AI use cases. The role ensures AI systems operate on governed, contextual, and reusable knowledge.

Requirements

10+ years in data architecture, semantic modeling, knowledge systems, or metadata engineering.
Deep expertise with ontologies, semantic technologies (RDF, OWL, SPARQL), and graph databases.
Strong knowledge of AI/ML data requirements, feature lineage, and model metadata.
Demonstrated experience with large-scale knowledge graph or semantic layer design.
Familiarity with explainability frameworks and AI trust principles.
Strong conceptual and systems thinking.
Ability to communicate technical concepts in business language.
Architectural storytelling and narrative-driven design.
Collaboration with cross-functional technical and non-technical teams.
Agile delivery mindset.
Bachelor’s or Master’s in Computer Science, Information Architecture, Data Science, or related field.

Nice To Haves

Experience with RAG, LLM fine-tuning, or vector search architectures.
Familiarity with metadata platforms (Purview, Collibra, Alation) or ML registries.
Exposure to MLOps workflows and AI governance frameworks.
Certifications in knowledge engineering, data architecture, or AI governance are advantageous.

Responsibilities

Design and maintain enterprise ontologies, taxonomies, and knowledge graph models.
Define data-to-model lineage, provenance, and explainability frameworks.
Create AI data contracts, semantic schemas, and metadata standards.
Architect RAG, semantic search, and AI knowledge store reference patterns.
Integrate governance, privacy, and compliance requirements into AI data flows.
Collaborate with AI Engineering, MLOps, Cloud, and Data Governance teams.
Develop templates, guidelines, and reusable patterns for accelerated AI enablement.
Evaluate and implement tools for semantic modeling, lineage, and AI trust frameworks.