Description

We are seeking a Knowledge Engineer to support the development of MAV for structured ingestion, reasoning, and retrieval of complex mining and asset information. The successful candidate will design and implement end-to-end knowledge extraction and ingestion pipelines, transforming unstructured and semi-structured technical documents (PDFs, reports, drawings) into RDF-based knowledge graphs, and enabling graph-native retrieval and reasoning workflows (GraphRAG). This role sits at the intersection of knowledge engineering, AI-driven extraction (LLM + vision), ontology design, and graph analytics.

Responsibilities

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).

Qualifications

- Proven experience as a Knowledge Engineer, Ontology Engineer, or Knowledge Graph Engineer.
- Solid 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 vision-based document processing (OCR, layout analysis, multimodal extraction).
- Experience designing retrieval pipelines from structured knowledge graphs.
- Familiarity with Azure-based AI and data platforms.
- Experience with GraphRAG or hybrid graph + LLM architectures.

BGV:

- Employment with WSP India is subject to the successful completion of a background verification (“BGV”) check conducted by a third-party agency appointed by WSP India.

- Candidates are advised to ensure that all information provided during the recruitment process — including documents uploaded — is accurate and complete, both to WSP India and its BGV partner”.

📌 Engineer - AI (Bengaluru)
🏢 Important Group
📍 Bengaluru