Knowledge Graph & Ontology Engineer (AI Knowledge Representation)

About iBusiness.ai

iBusiness.ai is a leading financial technology company helping banks, credit unions, and lenders modernize lending operations through secure AI, automation, and enterprise software. In the last decade the company's platforms have grown into supporting more than 50 financial institutions and have facilitated over $11 billion in lending volume.

As a pioneer in secure AI, automation, and AI software development, we build infrastructure and platforms that empower teams to modernize processes and work more efficiently without sacrificing compliance or security. Our workflow, verticalized, and point solutions enable seamless digital transformation, giving organizations of all sizes the tools they need to compete, innovate, and grow. 

Join us and be part of a team that’s building solutions designed to help businesses thrive!

Position Description

We are seeking an experienced Knowledge Graph & Ontology Engineer to design, implement, and govern the knowledge representation layer for next-generation AI systems. This role builds the foundational knowledge structures—ontologies, semantic models, knowledge graphs, provenance, and data fusion patterns—that enable AI agents and LLM applications to reason over enterprise knowledge reliably. You will collaborate closely with Retrieval/Relevance engineering, AI researchers, and data engineering to ensure our knowledge is well-structured, consistent, explainable, and evolvable.

Major Areas of Responsibility

Knowledge Representation & Semantic Modeling

• Develop and maintain ontologies, knowledge graphs, and semantic data models to structure and integrate domain knowledge for improved reasoning and downstream retrieval.
• Define canonical entities, relationships, attributes, and constraints, including taxonomy/controlled vocabularies and semantic definitions.
• Establish schema versioning, governance, and backward compatibility strategies to evolve the knowledge model safely.

Data Fusion & Knowledge Integration

• Aggregate disparate knowledge bases and heterogeneous data into a fused, consistent representation with clear semantics and lineage.
• Design integration patterns for structured + unstructured sources (e.g., documents → entities/relations) and maintain alignment across domains.

Provenance, Lineage, and Data Quality

• Define and enforce provenance/lineage standards (source attribution, timestamps, confidence, auditability).
• Collaborate with pipeline engineers to implement validation rules and quality gates for knowledge graph construction (e.g., integrity constraints, anomaly detection).
• Cognitive Memory & Persistent Knowledge Structures (Representation View)
• Design representation primitives that support cognitive memory architectures for AI agents (identity, episodic traces, persistent facts, context scoping).

Collaboration & Documentation

• Partner with Retrieval/Relevance engineering to define metadata contracts and “safe traversal” semantics for graph-aware retrieval.
• Maintain clear documentation of schemas, ontologies, knowledge modeling guidelines, and governance processes.
• Evaluate and integrate new technologies and research in knowledge representation and semantic modeling.

Required Knowledge, Skills, and Abilities

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field (or equivalent experience).
• Proven experience building knowledge graphs, semantic data models, and/or enterprise knowledge bases.
• Experience with semantic technologies and standards (as applicable): RDF, OWL, SPARQL (or equivalent graph/ontology concepts).
• Strong foundations in data modeling, entity resolution/canonicalization, and schema governance.
• Proficiency in Python and working with data pipelines (in collaboration with data engineering).
• Excellent analytical, problem-solving, and cross-functional communication skills.

Nice To Haves

• Experience designing agent memory representations (episodic/semantic memory patterns, long-term context).
• Familiarity with LLM grounding patterns (provenance, citations, trust signals).
• Experience with graph databases and tooling (e.g., Neo4j/AWS Neptune equivalents).
• Experience with data-centric AI and training data quality assessment.

Primary Ownership (What success looks like)

• The knowledge model is correct, consistent, explainable, and governable.
• High-quality entity resolution + clean relationships + strong provenance coverage.
• Stable schemas that evolve without breaking downstream applications.

Conclusion: 

This job description is intended to convey information essential to understanding the scope of the job and the general nature and level of work performed by job holders within this job. This job description is not intended to be an exhaustive list of qualifications, skills, efforts, duties, responsibilities, or working conditions associated with the position. 

The company is an equal opportunity employer and will consider all applications without regard to race, sex, age, color, religion, national origin, veteran status, disability, genetic information, or any other characteristic protected by law.