WSP

1 week ago

Engineer - AI

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Source: Company careers pageValidated by: Fursa AI

Last checked: 20 hours agoStatus: Live

Job Summary

Design and implement automated knowledge extraction and ingestion pipelines that transform technical mining and asset documents (PDFs, scans, reports, drawings) into RDF-based knowledge graphs. Build and evolve ontologies and schemas using RDF/OWL, manage RDF graph repositories, and implement graph-native retrieval and GraphRAG workflows combining SPARQL, reasoning, and embeddings. Collaborate with development, domain experts, and AI engineers and integrate cloud AI services such as Azure AI and OpenAI models.

Location: Bengaluru, Mumbai

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Design and implement automated extraction pipelines for TSF documents (PDFs, scanned reports, tables, figures).
•Use LLM-based and vision-based techniques (OCR, layout understanding, multimodal models) to extract entities, attributes, relationships, and evidence.
•Design and evolve domain ontologies and knowledge schemas using RDF/OWL to structure TSF, risk, asset, and operational data.
•Build and manage RDF-based knowledge graphs in repositories (GraphDB, RDFox, Neptune, or equivalent), including ingestion with traceability and provenance/evidence linking.
•Create graph-native retrieval and GraphRAG pipelines using SPARQL, reasoning, and embeddings; enable natural-language querying over the knowledge graph and support integration with cloud AI services (e.g., Azure AI, OpenAI).

Key Requirements

•Proven experience as a Knowledge Engineer, Ontology Engineer, or Knowledge Graph Engineer.
•Strong understanding of RDF, OWL, SPARQL, and semantic data modeling.
•Hands-on experience with RDF-based graph repositories (e.g., GraphDB, RDFox, Apache Jena, Neptune).
•Experience designing automated knowledge extraction pipelines using LLMs.
•Familiarity with vision-based document processing (OCR, layout analysis, multimodal extraction) and GraphRAG/hybrid graph + LLM architectures.

Experience:MiningKnowledge graphsAILLM-based extractionGraphRAG

Languages:US

Tech Stack:RDFOWLSPARQLSemantic data modelingGraphDBRDFoxApache JenaNeptuneLLMsOCRLayout analysisMultimodal extractionGraph-native retrievalGraphRAGEmbeddingsAzure AIOpenAI models

Company Brief

WSP

Global professional services firm providing engineering, consulting, and technical services across infrastructure, environment, buildings, transportation, and energy sectors for public- and private-sector clients worldwide.

Industry: Professional Services

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Montreal, Canada

Founded: 1959

Website LinkedIn

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WSP