Engineer - AI

Knowledge Extraction & Ingestion

• Design and implement automated extraction pipelines for technical tailings storage facility (TSF) documents (PDFs, scanned reports, tables, figures).
• Apply LLM-based and vision-based extraction techniques (OCR, layout understanding, multimodal models) to identify entities, attributes, relationships, and evidence.
• 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).