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Knowledge Graph Engineer

ID pozice
510871
Zveřejněno od
18-Čer-2026
Organizace
Siemens Energy
Obor
Research & Development
Společnost
SIEMENS ENERGY INDIA LIMITED
Úroveň zkušeností
S dlouholetou praxí v oboru
Typ pozice
Plný úvazek
Režim práce
Pouze na pracovišti
Druh smlouvy
Trvalý
Lokalita
  • Bengalúru - Karnataka - Indie
  • Gurugrám - Haryana - Indie
  • Puné - Maharashtra - Indie
Title : Knowledge Graph Engineer

Position Summary
We are building a connected enterprise knowledge layer that unifies structured and unstructured data across business systems and enables intelligent search, contextual retrieval, semantic reasoning, and AI-driven workflows. In this role, you will design and implement scalable knowledge graph solutions that model business entities, relationships, and domain logic to support advanced analytics, semantic applications, and next-generation AI use cases. This is a hands-on engineering role spanning graph modeling, ontology development, semantic enrichment, and enterprise data integration. 

How You’ll Make an Impact (Responsibilities of Role)

Knowledge Graph Design & Engineering
• Design scalable knowledge graph schemas using property graph and/or RDF-based models. 
• Develop and optimize graph queries using Cypher, SPARQL, or Gremlin. 
• Model business entities, relationships, hierarchies, and context across domains. 
• Build ingestion pipelines to transform enterprise data into graph structures. 

Semantic Modeling & Ontology Development
• Create and maintain ontologies, taxonomies, and semantic models. 
• Define canonical entity models and semantic mappings across data sources. 
• Establish semantic validation, consistency standards, and data quality checks. 
• Support ontology lifecycle management and schema evolution. 

Data Integration & Semantic Enrichment
• Collaborate with engineering teams to ingest, transform, and enrich enterprise data. 
• Support entity resolution, metadata enrichment, and relationship extraction. 
• Enable semantic search, intelligent assistants, and knowledge-driven workflows. 

APIs, Collaboration & Platform Enablement
• Design and support graph APIs and semantic access layers. 
• Partner with product, architecture, security, and domain teams on graph solutions. 
• Document graph modeling standards, patterns, and best practices. 

Quality, Governance & Performance
• Optimize query performance, indexing, and traversal efficiency. 
• Contribute to metadata, lineage, governance, and access control practices. 
• Ensure graph solutions are scalable, secure, and aligned with enterprise data standards. 


What You Bring (Required Qualifications and Skill Sets)

• Bachelor’s/master’s degree in computer science, Data Science, Engineering, Information Systems, Mathematics, or a related field. 
• 5–7 years of experience in knowledge graph engineering, graph databases, semantic modeling, ontology engineering, or related data architecture roles.
• Strong hands-on experience with at least one graph platform such as Neo4j, AWS Neptune, Stardog, TigerGraph, GraphDB, or similar technologies. 
• Proficiency in graph query languages such as Cypher, SPARQL, or Gremlin. 
• Experience designing graph schemas, semantic data models, taxonomies, and ontology-aligned structures for enterprise use cases. 
• Good understanding of knowledge graphs, RDF, OWL, semantic web concepts, ontology design, and linked data principles. 
• Experience integrating enterprise data from sources such as relational databases, APIs, document repositories, cloud platforms, and business applications. 
• Strong skills in Python and SQL for data transformation, graph ingestion, enrichment, and query support.
• Familiarity with data governance, metadata management, lineage, access control, and enterprise data quality practices. 
• Ability to work cross-functionally with engineers, architects, business stakeholders, and domain experts to translate business concepts into scalable graph models. 
Preferred Qualifications
• Experience with ontology tools such as Protégé and semantic validation frameworks such as SHACL or similar approaches. 
• Exposure to inference, reasoning engines, rule-based modeling, or semantic constraint design. 
• Experience building enterprise knowledge graphs for semantic search, AI copilots, document intelligence, workflow automation, or recommendation engines. 
• Familiarity with vector search, RAG, hybrid graph + AI architectures, or semantic retrieval patterns. 
• Exposure to cloud environments such as AWS, Azure, or GCP in support of graph deployment and enterprise integration. 
• Understanding of observability, graph query tuning, and semantic layer performance monitoring is a plus.