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Design, implement, and maintain production knowledge graph architectures using Neo4j while building automated ingestion pipelines. Integrate these graphs with downstream AI/ML workflows and agentic systems to ensure data quality and performance.
This is a remote position.
We are seeking a Knowledge Graph Engineer to build, optimize, and scale our graph data infrastructure and ingestion pipelines.
Key Responsibilities:
Design, implement, and maintain production knowledge graph architectures using Neo4j.
Build automated ETL/ELT ingestion pipelines to map relational, semi-structured, and unstructured data into scalable graph schemas.
Write high-performance Cypher queries and optimize graph indexing, traversal speeds, and memory usage.
Integrate knowledge graphs with downstream AI/ML workflows and agentic systems (GraphRAG, vector + graph hybrid search).
Implement data quality checks, entity resolution, and graph lineage monitoring.
Work Schedule: During the onboarding phase, working hours may be adjusted to overlap with US hours until 7:00 PM at the latest.
3+ years of hands-on experience designing and operating graph databases, with a strong preference for Neo4j (Aura / Enterprise).
Expert-level proficiency in Cypher and a solid understanding of graph data modeling (Labeled Property Graphs vs. RDF).
Strong Python and SQL skills for data transformation, entity linking, and pipeline automation.
Experience integrating graph databases with modern cloud data platforms (e.g., Databricks, AWS, Azure) or LLM frameworks (LangChain, LlamaIndex).
Familiarity with semantic modeling concepts and ontology-driven schemas is a strong plus.
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