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Avathon

AI-driven cybersecurity and operational optimization.

Lead Knowledge Graph Engineer - Computational Knowledge Graph

Backend EngineeringConfirmed live in the last 24 hours
Full-timePleasanton, CA, USA

About the Job

Requirements

  • Five to seven years of software engineering experience, including at least two years of hands-on experience with graph databases or knowledge graphs.
  • Strong Python skills.
  • Hands-on C++ experience, ideally with performance-sensitive or systems-level code.
  • Production experience with a graph database or knowledge graph platform such as JanusGraph, Neo4j, Amazon Neptune, or TigerGraph.
  • Experience writing and optimizing Gremlin traversals using Apache TinkerPop, preferably on JanusGraph.
  • A solid grasp of ontology design, schema modeling, and graph data structures.
  • Strong SQL skills and a track record of query performance tuning.
  • Experience designing and building REST APIs and service-based architectures.
  • Experience with at least one major cloud platform; Google Cloud Platform is preferred, while AWS or Azure is acceptable.
  • Ability to turn ambiguous business requirements into clear technical designs.
  • Clear communication and experience working with cross-functional teams.
  • A BS or MS in Computer Science, Engineering, or a related field, or equivalent practical experience.

Responsibilities

  • Design knowledge graph schemas, ontologies, and data models for supply chain, manufacturing bill of materials, and logistics data.
  • Create and maintain pipelines that bring data from enterprise systems such as SAP, databases, and files into the graph, with appropriate data quality checks; build connectors for both information technology and operational technology systems.
  • Develop multi-hop traversals, joins, and aggregations in the graph query language IQL and in Gremlin, and tune them for datasets with millions of records; add graph algorithms for faster multi-hop traversal.
  • Develop and optimize performance-critical graph and compute components in C++; improve the graph processing engine to support large datasets.
  • Build computational functions and graph analytics, and connect the graph to Python and NumPy workflows for advanced analytics and machine learning model integration.
  • Design and build APIs and services that enable downstream applications to use the knowledge graph.
  • Partner with product and data science teams to turn business problems into graph-based solutions.
  • Coach engineers on graph modeling, query design, and platform best practices, and contribute to architecture decisions and the technical roadmap.

Desired Qualifications

  • Experience with RDF, SPARQL, or other semantic web technologies.
  • Experience with JanusGraph storage and index backends such as Cassandra, HBase, Bigtable, or Elasticsearch.
  • Experience building natural language processing or named-entity recognition pipelines for extracting entities and relationships.
  • Experience with machine learning model integration and MLOps.
  • Experience with graph databases or knowledge graph platforms such as Neo4j, Amazon Neptune, TigerGraph, or similar.
  • Experience with time-series data or Internet of Things integration.
  • Working knowledge of graph algorithms such as shortest path, centrality, and community detection.
  • Experience visualizing graph data.
  • Contributions to open-source graph or knowledge-management projects.

About the Company

Avathon

AI-driven cybersecurity and operational optimization.

Avathon develops AI-powered software and services to improve security, efficiency, and productivity across industries such as manufacturing, energy, finance, and defense. Its solutions protect digital assets, optimize operations, and help organizations make data-driven decisions, including cognitive endpoint protection and NLP-driven document analysis. The company offers software licenses or subscriptions plus professional services to implement customized AI solutions like anomaly detection, predictive maintenance, and fleet management. Avathon differentiates itself through interdisciplinary collaboration and applying AI to real-world workflows, aiming to help large organizations secure data, unlock insights, and drive growth in the AI-enabled economy.

Company size
201-500
Company stage
Series D
Total funding
$286.6M
Headquarters
Pleasanton, CA
Founded
2013

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