Semantic AI/ML Engineer-Web Ontology Language (Immediate joiner with short NP within 30 days)

Project Description: We are seeking a Senior Semantic Engineer to implement semantic data frameworks that provide a shared structure for enterprise data. In this role you will focus on building and maintaining ontologies and knowledge graphs, enforcing semantic validation rules for data quality, and collaborating with AI teams to integrate these semantic structures into intelligent applications. The position is industry-agnostic, emphasizing strong semantic web expertise and the ability to apply it in any enterprise context.

Responsibilities: • Ontology Design & Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar) that model key enterprise data domains and relationships, ensuring a consistent and shared data vocabulary across the organization. This includes collaborating with domain experts to capture real-world concepts and validate that the ontology accurately represents business knowledge. • Knowledge Graph Development: Build and manage enterprise knowledge graphs based on the defined ontologies, linking diverse data sources into a unified graph data model. This involves configuring graph databases or triple stores, populating the knowledge graph with data (RDF triples), and optimizing it for query performance and scalability. • Semantic Querying (SPARQL): Create and optimize SPARQL queries to enable efficient retrieval, integration, and analysis of data from the knowledge graph.

You will develop semantic queries and endpoints that support advanced search and analytics use cases, making it easier for others to retrieve insights from linked data. • Integration with AI Agents: Work with AI agents and large language model (LLM) teams to leverage the ontology and knowledge graph for intelligent applications. For instance, you might enable an AI chatbot to use the knowledge graph for more context-aware responses, or develop mechanisms for AI systems to perform reasoning over the ontologies. This responsibility ensures that semantic data structures enhance AI initiatives (e.g. improving context, disambiguation, and knowledge retrieval in AI workflows).

Mandatory Skills Description: Ontology Design & Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar). Semantic Web Proficiency: Strong knowledge of semantic web technologies and standards - specifically, hands-on proficiency with OWL (Web Ontology Language) and RDF (Resource Description Framework) for ontology modelling, as well as SPARQL for querying graph data. Knowledge Graph Experience: Practical experience building or maintaining knowledge graphs or linked data systems in an enterprise setting.

Data Modelling & Integration Skills: A solid understanding of data modelling principles, data architecture, and integrating heterogeneous data sources. You should be capable of abstracting real-world entities into a semantic schema and mapping relational or NoSQL data to an ontology. Programming Skills: Proficiency in at least one programming or scripting language (such as Python, Java, or similar)