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Overnight Knowledge Graph Software Engineer Jobs

GEICO is seeking an experienced Staff Software Engineer to join our Knowledge Graph and Content Generation engineering group. This is a high-impact team focused on scaling GEICO's intelligent content ...

Senior Data Engineer - Knowledge Graphs

Herndon, VA ยท On-site

$117.70K - $141.40K/yr

Partner with AI/ML, platform, and software engineering teams to ensure graph and semantic data ... Demonstrated experience with knowledge graphs, graph data models, or semantic data architectures

... Software is driving transformation to enhance the digital enterprise where engineering ... With the 2025 acquisition of Altair Graph Studio , Siemens now offers a Knowledge Graph platform ...

Partner with AI/ML, platform, and software engineering teams to ensure graph and semantic data ... Demonstrated experience with knowledge graphs, graph data models, or semantic data architectures

Senior Data Engineer - Knowledge Graphs

Reston, VA ยท On-site

$119.10K - $143K/yr

Partner with AI/ML, platform, and software engineering teams to ensure graph and semantic data ... Demonstrated experience with knowledge graphs, graph data models, or semantic data architectures

Senior Data Engineer - Knowledge Graphs

Herndon, VA

$117.70K - $141.40K/yr

Partner with AI/ML, platform, and software engineering teams to ensure graph and semantic data ... Demonstrated experience with knowledge graphs, graph data models, or semantic data architectures

Senior Graph AI Engineer

North Chicago, IL ยท On-site

$117.80K - $155.30K/yr

Designing and managing knowledge graphs * Graph data modeling, knowledge graph design, ontology ... Prompt engineering and context orchestration * Building GenAI apps using LangChain / LlamaIndex

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Overnight Knowledge Graph Software Engineer information

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$63.5K

$147.5K

$205.5K

How much do overnight knowledge graph software engineer jobs pay per year?

As of May 30, 2026, the average yearly pay for overnight knowledge graph software engineer in the United States is $147,524.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,000.00 and $173,000.00 per year, depending on experience, location, and employer.

What is the difference between Overnight Knowledge Graph Software Engineer vs Data Engineer?

AspectOvernight Knowledge Graph Software EngineerData Engineer
CredentialsBachelor's or higher in CS, experience with graph databasesBachelor's or higher in CS, experience with data pipelines
Work EnvironmentDeveloping and maintaining knowledge graph systems overnightBuilding and managing data pipelines during regular hours
Industry UsageTech, AI, knowledge management companiesFinance, tech, healthcare, and data-driven industries

The Overnight Knowledge Graph Software Engineer focuses on developing and maintaining knowledge graph systems during overnight shifts, often working with graph databases and semantic data. In contrast, Data Engineers build and manage data pipelines and infrastructure during regular hours. Both roles require strong technical skills, but their focus areas and work schedules differ significantly.

What cities are hiring for Overnight Knowledge Graph Software Engineer jobs? Cities with the most Overnight Knowledge Graph Software Engineer job openings:
What are the most commonly searched types of Knowledge Graph Software Engineer jobs? The most popular types of Knowledge Graph Software Engineer jobs are:
What states have the most Overnight Knowledge Graph Software Engineer jobs? States with the most job openings for Overnight Knowledge Graph Software Engineer jobs include:
Knowledge Graph & Ontology Engineer (AI Knowledge Representation)

Knowledge Graph & Ontology Engineer (AI Knowledge Representation)

iBusiness Funding

Fort Lauderdale, FL โ€ข Remote

Full-time

Posted 6 days ago


Job description

Salary:

About iBusiness

iBusinessis a leading financial technology company transforming the way banks, credit unions, and lenders innovate. As apioneerinsecureAI, automation, and AI software development,iBusinessbuilds infrastructure and platforms that empower financial institutions to modernize fasterwithout sacrificing compliance or security. Its technologyenablesseamless digital transformation across lending, banking, and customer experience systems, giving institutions the tools to compete and innovate at enterprise scale.

Join us and be part of a team thats transforming the finance industry and empowering businesses to 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 structuresontologies, semantic models, knowledge graphs, provenance, and data fusion patternsthat 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

  • Bachelors or Masters 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.