Sr. Engineer, Knowledge Engineering
New
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of software engineering experience, including at least 2+ years focused on knowledge graphs, data modeling, or semantic systems
- Required Skills
- PythonElasticSearchData modeling
Requirements
- 5+ years of software engineering experience, including at least 2+ years focused on knowledge graphs, data modeling, or semantic systems.
- Strong experience with graph databases or graph query technologies such as Neo4j, Amazon Neptune, or Elasticsearch graph features.
- Solid understanding of ontology design principles, including RDF, JSON-LD, OWL, or similar semantic modeling approaches.
- Experience building entity resolution systems and populating knowledge graphs from structured enterprise data sources.
- Strong data modeling skills, including entity-relationship design, hierarchical taxonomies, and schema design.
- Proficiency in Python and graph query languages such as Cypher, Gremlin, SPARQL, or Elasticsearch DSL.
- Experience working with enterprise-scale B2B SaaS data models is a strong plus.
- Bachelor’s or Master’s degree in Computer Science or equivalent practical experience.
- Strong analytical thinking, collaboration skills, and ability to work in complex, data-rich environments.
Responsibilities
- Design and implement ontologies based on complex enterprise data models, defining entities, relationships, and business rules across spend management domains.
- Build and maintain scalable knowledge graphs that represent structured relationships between business entities such as suppliers, contracts, invoices, and purchase orders.
- Develop graph query interfaces and APIs to enable AI systems and applications to consume and reason over knowledge graph data.
- Design and evolve multi-tenant and shared knowledge graph architectures, ensuring proper isolation, scalability, and performance.
- Map existing taxonomies, classifications, and hierarchical structures into ontology-driven graph representations.
- Collaborate with ML and AI engineering teams to generate ontology-aware datasets for model training and evaluation.
- Evaluate and select appropriate graph storage and query technologies (e.g., Neo4j, Neptune, Elasticsearch graph capabilities) based on scalability and performance needs.
- Continuously evolve and refine the ontology framework as new business domains and AI capabilities are introduced.
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