Experienced Semantic Software Engineer to design and develop software solutions that leverage semantic technologies, ontologies, and structured knowledge models.
The role focuses on enabling semantic interoperability, knowledge-driven applications, and standards-based data exchange across enterprise platforms.
Roles & Responsibilities:
· Design and develop semantic-enabled software components and services
· Implement solutions that leverage ontologies, knowledge models, and semantic metadata
· Integrate semantic layers with APIs, backend services, and data platforms
· Develop and maintain semantic data processing and validation logic
· Collaborate with ontologists, architects, and domain experts to translate semantic models into cutable solutions
· Support semantic interoperability across systems and platforms
· Ensure semantic consistency across data ingestion, transformation, and consumption layers
· Participate in design reviews, code reviews, and architecture discussions
· Troubleshoot and resolve issues related to semantic data interpretation and integration
· Follow software engineering best practices for coding, testing, and documentation
· Strong understanding of software development principles and SDLC
· Hands-on experience working with structured data models and metadata-driven systems
· Experience integrating semantic models with backend services or APIs
· Familiarity with ontology-driven or standards-based data representations
· Strong problem-solving, analytical, and debugging skills
· Ability to collaborate with both technical and domain stakeholders
Generic Managerial Skills, If any
Experience working in agile development projects and sprint deliver
Role Descriptions: Practical experience implementing semantic data virtualization (e.g.| R2RML mappings) and integrating knowledge graphs with analytics pipelines.Proficiency with semantic stores reasoners and building scalable SPARQLGraphQL or API based semantic services.
Essential Skills: Practical experience implementing semantic data virtualization (e.g.| R2RML mappings) and integrating knowledge graphs with analytics pipelines.Proficiency with semantic stores reasoners and building scalable SPARQLGraphQL or API based semantic services.