Principle – Knowledge Governance & GenAI Enablement.
Tasks
- Align ontologies knowledge graphs and semantic models
- Define domain ownership and stewardship models
- Define onboarding processes for GenAI use cases
- Define semantic modeling standards for BI and AI
- Design and evolve enterprise ontologies
- Detect and mitigate bias and leakage
- Detect and mitigate semantic drift
- Embed governed knowledge into RAG systems
- Embed governed knowledge into agent frameworks
- Embed governed knowledge into knowledge graphs
- Enforce governance for accuracy provenance compliance auditability
- Enforce knowledge lifecycle creation validation approval publication monitoring retirement
- Ensure interoperability across platforms and AI systems
- Establish ontology modeling standards and versioning
- Govern enterprise semantic standards
- Improve retrieval precision and recall
- Manage semantic change versioning and approvals
- Own enterprise semantic layer in Snowflake
- Reduce hallucinations with semantic foundations
- Reduce semantic inconsistencies in reporting and AI
- Set guardrails for structured and unstructured knowledge
- Standardize business definitions and metrics
Perks/Benefits
Skills/Tech-stack
Agent systems | Auditability | Compliance | Data Governance | Data Lineage | Data Modeling | Knowledge graphs | Metadata Management | Ontology Design | Provenance | RAG | Retrieval evaluation | Semantic Modeling | Snowflake
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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