Knowledge Extraction & Ingestion
- Design and implement automated extraction pipelines for technical tailings storage facility (TSF) documents (PDFs, scanned reports, tables, figures).
- Develop validation and normalization logic to ensure extracted knowledge meets quality and consistency requirements.
Knowledge Schema & Ontology Design
- Design and evolve domain ontologies and knowledge schemas to support structured storage of TSF, risk, asset, and operational data.
- Implement schemas using RDF/OWL, including classes, properties, constraints, and semantic relationships.
- Align schemas with industry standards and internal MAV data models.
Knowledge Graph Development
- Build and manage RDF-based knowledge graphs in graph repositories (e.g., GraphDB, RDFox, Neptune, or equivalent).
- Implement ingestion workflows that map extracted content into graph structures with traceability to source documents.
- Support versioning, provenance, and evidence linking within the knowledge graph.
Retrieval & GraphRAG Pipelines
- Design and implement graph-native retrieval pipelines,
combining SPARQL queries, reasoning, and embeddings where appropriate.
- Develop GraphRAG architectures that leverage structured graph context rather than flat text retrieval.
- Enable natural-language querying over the knowledge graph for downstream AI assistants and analytics tools.
Collaboration & Integration
- Work closely with development team, domain experts, and AI engineers to refine extraction logic and schema requirements.
- Support integration with cloud AI services (e.g., Azure AI, OpenAI models, document processing services).
- Proven experience as a Knowledge Engineer, Ontology Engineer, or Knowledge Graph Engineer.
- Robust understanding of RDF, OWL, SPARQL, and semantic data modeling.
- Hands-on experience with RDF-based graph repositories (GraphDB, RDFox, Apache Jena, Neptune, etc.).
- Experience designing automated knowledge extraction pipelines using LLMs.
- Familiarity with visio
📌 Engineer - Ai (Bengaluru)
🏢 Wsp
📍 Bengaluru