Knowledge Graph Engineer
Domyn
- India
- Permanent
- Full-time
- Select graph stores and pipeline components; design reference architectures (RDF/OWL/SHACL/SPARQL, streaming, MDM, AI/LLM enrichment) that ensure scalability, security, lineage, and cloud portability (Azure / AWS / GCP).
- Implement ingestion and ELT jobs (Python, Apache Jena, RDF4J, etc.); configure inference rules, load data, tune SPARQL queries, and automate regression/performance tests.
- Build REST/GraphQL-LD wrappers and sample applications to expose semantic services; work with product teams and data scientists to embed graph capabilities into downstream apps and GenAI/RAG pipelines.
- Translate complex graph concepts for C-suite, business SMEs, and engineering teams; run workshops, build adoption playbooks, and align roadmaps with measurable business outcomes.
- Define and maintain a multi-year knowledge-graph roadmap that supports organisation-wide use cases (search, recommendation, analytics, GraphRAG, GenAI grounding, compliance).
- 5 + years designing or implementing production knowledge graphs; deep hands-on experience with at least one commercial semantic platform (Metaphacts, TopQuadrant, Stardog, GraphDB, Neptune, etc.).
- Expert knowledge of RDF/OWL, SPARQL, SHACL, linked-data patterns, and graph-query optimisation.
- Proven track record running vendor PoCs and presenting buy-vs-build assessments that cover fit, cost, risk, and roadmap.
- Strong cloud data-architecture skills (Azure, AWS, or GCP) and fluency in Python or Java for semantic ETL, API development, and DevOps (Docker, CI/CD).
- Executive-level communication and stakeholder-alignment abilities; comfortable interfacing with C-suite and cross-functional teams.
- Experience embedding knowledge graphs into LLM / RAG pipelines for grounding and retrieval.
- Familiarity with regulated-industry data governance (e.g., GDPR, FCA) or financial-services data models.
- Exposure to GraphQL-LD, ontology-driven API generation, or automated schema evolution tooling.
- Background in streaming data ingestion (Kafka, Kinesis) and real-time semantic reasoning.
- Contributions to open-source semantic-web projects or academic publications in knowledge-graph research.