Onthology Specialist

Daimler Truck Innovation Center India (DTICI); Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Daimler Truck Innovation Center India (DTICI) is seeking an Ontology Specialist to design and operationalize enterprise-scale knowledge graphs that transform fragmented data into connected, AI-ready assets. This role sits at the intersection of data architecture, semantic modeling, knowledge graphs, and AI enablement.

You will collaborate with business, data, and AI teams to create reusable semantic models powering analytics, intelligent decision-making, recommendation engines, digital twins,

Qualifications

• 12+ years of experience in data engineering/architecture.
• At least 3 years hands-on in enterprise knowledge graphs, ontology modeling, and semantic architecture.
• Strong expertise in RDF, OWL, SPARQL, SHACL, and SKOS.
• Hands-on experience with graph databases such as Neo4j.
• Experience with lakehouse/data lake architectures, Azure data services, and engineering stacks including Python, PySpark, SQL and orchestration pipelines.
• Experience with GraphRAG, vector databases, and integration with Azure OpenAI/LLM-based systems.

Responsibilities

• Lead the design and implementation of enterprise ontologies and knowledge graphs across domains such as supply chain, manufacturing, and operations.
• Define graph schemas and semantic models that align to enterprise data platforms, analytics use cases, and downstream consumption layers such as Power BI and AI applications.
• Build and optimize scalable graph architectures, including indexing, partitioning, caching, and query performance tuning.
• Integrate structured, semi-structured, and unstructured data into semantic platforms using Python, Spark, PySpark, and orchestration frameworks.
• Develop graph queries, traversal logic, and analytics using SPARQL, Cypher, Gremlin, and KQL graph semantics.
• Enable real-time ingestion, relationship extraction, graph enrichment, and semantic access patterns for downstream systems and business applications.
• Integrate knowledge graphs with Azure OpenAI, LLM-based systems, and Copilot or agentic AI frameworks to enable semantic grounding and GraphRAG-style retrieval.
• Expose ontology and graph data to analytics platforms, recommendation engines, AI/ML pipelines, and enterprise applications through reusable and scalable integration patterns.
• Establish governance for ontology lifecycle management, semantic consistency, data lineage, stewardship, and versioning in collaboration with domain experts.
• Act as a technical authority and thought partner for ontology, knowledge graph, and semantic architecture initiatives across the organization.

Skills

Semantic web technologies

Knowledge graphs

Graph data modeling

Graph querying

Python

PySpark

SQL

Cypher

SPARQL

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, Information Systems or related field

Tools

Neo4j

Python

Spark

PySpark

SQL

Cypher

Gremlin

SPARQL

KQL

SHACL

Job description

We are looking for a highly skilled Ontology Specialist to help shape the semantic backbone of our enterprise Data & AI ecosystem. In this role, you will design, build, and operationalize enterprise-scale knowledge graphs and ontologies that transform fragmented data into connected, contextual, and AI-ready assets.

This role sits at the intersection of data architecture, semantic modeling, knowledge graphs, and AI enablement. You will work closely with business, data, and AI teams to create reusable semantic models that power advanced analytics, intelligent decision-making, recommendation engines, digital twins, and AI-driven experiences.

Responsibilities:

• Lead the design and implementation of enterprise ontologies and knowledge graphs across domains such as supply chain, manufacturing, and operations.
• Define graph schemas and semantic models that align to enterprise data platforms, analytics use cases, and downstream consumption layers such as Power BI and AI applications.
• Build and optimize scalable graph architectures, including indexing, partitioning, caching, and query performance tuning.
• Integrate structured, semi-structured, and unstructured data into semantic platforms using modern data engineering tools such as Python, Spark, PySpark, and orchestration frameworks.
• Develop graph queries, traversal logic, and analytics using technologies such as SPARQL, Cypher, Gremlin, and KQL graph semantics to uncover cross-domain insights.
• Enable real-time ingestion, relationship extraction, graph enrichment, and semantic access patterns for downstream systems and business applications.
• Integrate knowledge graphs with Azure OpenAI, LLM-based systems, and Copilot or agentic AI frameworks to enable semantic grounding and GraphRAG-style retrieval.
• Expose ontology and graph data to analytics platforms, recommendation engines, AI/ML pipelines, and enterprise applications through reusable and scalable integration patterns.
• Establish governance for ontology lifecycle management, semantic consistency, data lineage, stewardship, and versioning in collaboration with domain experts and business stakeholders.
• Act as a technical authority and thought partner for ontology, knowledge graph, and semantic architecture initiatives across the organization.

What we’re looking for

• Bachelor’s or master’s degree in computer science, Data Science, Information Systems, or a related field.
• 12+ years of experience in data engineering, data architecture, or related areas, with at least 3 years of hands-on experience in enterprise knowledge graphs, ontology modeling, and semantic architecture.
• Strong expertise in semantic web technologies including RDF, OWL, SPARQL, SHACL, and SKOS, along with experience using ontology modeling tools.
• Hands-on experience with graph databases such as Neo4j or similar platforms, and strong working knowledge of Cypher, Gremlin, SPARQL, and KQL graph semantics.
• Practical experience with lakehouse or data lake architectures, Microsoft Fabric or Azure data services, and engineering stacks including Python, PySpark, SQL, and orchestration pipelines.
• Experience with GraphRAG, vector databases, hybrid retrieval architectures, and integration of semantic platforms with Azure OpenAI or other LLM-based systems.

Nice to have:

Experience with platforms such as Stardog or Ontotext, exposure to Data Mesh or semantic layer architectures, and domain knowledge in automotive or manufacturing.

If you’re passionate about building semantic foundations for the next generation of enterprise AI, analytics, and intelligent decision-making, we’d love to hear from you.