Mufg
Financial Services
AIKnowledgeIntelligenceFoundationsArchitect,VP
Neural analysis suggests this role is
optimal for VP candidates.
“AI Knowledge Intelligence Foundations Architect, VP at Mufg. Skills: Ontologies, Semantic modeling, Knowledge graphs, AI frameworks. Design enterprise ontologies. Maintain enterprise ontologies”
What You'll Achieve.
Enable responsible AI; Enable explainable AI; Enable enterprise-ready AI; Ensure AI systems operate on governed knowledge; Ensure AI systems operate on contextual knowledge; Ensure AI systems operate on reusable knowledge; Accelerate AI enablement
Industry & Context.
Conceptual thinking; Systems thinking
What They're Looking For.
Must Have
5+ years in data architecture, semantic modeling, knowledge systems, metadata engineering, ontologies, semantic technologies (RDF, OWL, SPARQL), graph databases, AI/ML data requirements, feature lineage, model metadata, large-scale knowledge graph or semantic layer design, explainability frameworks, AI trust principles, Bachelor’s or Master’s in Computer Science, Information Architecture, Data Science, related field
Nice to Have
RAG, LLM fine-tuning, vector search architectures, metadata platforms (Purview, Collibra, Alation), ML registries, MLOps workflows, AI governance frameworks, knowledge engineering certifications, data architecture certifications, AI governance certifications
What You'll Do.
Design enterprise ontologies
Maintain enterprise ontologies
Design knowledge graph models
Maintain knowledge graph models
Define data-to-model lineage
Define explainability frameworks
Create AI data contracts
Create semantic schemas
Create metadata standards
Architect RAG patterns
Architect semantic search patterns
Architect AI knowledge store patterns
Integrate governance requirements
Integrate privacy requirements
Integrate compliance requirements
Collaborate with AI Engineering
Collaborate with MLOps
Collaborate with Cloud teams
Collaborate with Data Governance teams
Develop reusable patterns
How You'll Work.
Team & Collaboration
Collaborate with AI Engineering teams; Collaborate with MLOps teams; Collaborate with Cloud teams; Collaborate with Data Governance teams; Collaborate with cross-functional technical teams; Collaborate with non-technical teams
Communication Scope
Communicate technical concepts in business language; Architectural storytelling; Narrative-driven design
Process & Methodology
Agile delivery mindset
Full Job Description
**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. ## Major 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 reusab
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