Engineer - AI

WSP
Noida, Mumbai
Full timeFunction: Data Science & Machine LearningSkills: ["Collaboration"]

Design and build end-to-end AI knowledge pipelines for structured ingestion and retrieval of complex mining and asset information. You’ll develop LLM- and vision-based extraction from PDFs and technical documents, normalize the extracted data into domain ontologies/schemas, and store it as RDF knowledge graphs. Build graph-native retrieval and GraphRAG workflows using SPARQL, reasoning, and embeddings, and integrate with cloud AI services to power downstream AI assistants and analytics.

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FursaFursa
WSP
WSP
2 days ago

Engineer - AI

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 3 hours agoStatus: Live

Job Summary

Design and build end-to-end AI knowledge pipelines for structured ingestion and retrieval of complex mining and asset information. You’ll develop LLM- and vision-based extraction from PDFs and technical documents, normalize the extracted data into domain ontologies/schemas, and store it as RDF knowledge graphs. Build graph-native retrieval and GraphRAG workflows using SPARQL, reasoning, and embeddings, and integrate with cloud AI services to power downstream AI assistants and analytics.
Location: Noida, Mumbai
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Design and implement automated knowledge extraction pipelines for TSF technical documents (PDFs, scanned reports, tables, figures).
  • •Apply LLM- and vision-based techniques to extract entities, attributes, relationships, and evidence, then validate and normalize the output.
  • •Design and evolve domain ontologies and knowledge schemas, implementing them with RDF/OWL and aligning with internal MAV data models.
  • •Build and manage RDF-based knowledge graphs and ingestion workflows with traceability, provenance, and versioning.
  • •Create graph-native retrieval and reasoning workflows (GraphRAG), enabling natural-language querying over the knowledge graph for downstream AI tools.

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 such as GraphDB, RDFox, Apache Jena, or Neptune.
  • •Experience designing automated knowledge extraction pipelines using LLMs, including vision-based document processing (OCR, layout analysis, multimodal extraction).
  • •Familiarity with Azure-based AI and data platforms, and with GraphRAG or hybrid graph + LLM architectures.
Experience:Mining
Skills:Collaboration
Tech Stack:LLMOCRRDFOWLSPARQLGraphDBRDFoxApache JenaNeptuneGraphRAGEmbeddingsAzure AIOpenAIMultimodalLayout understandingVision-based document processingDocument processing services

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
WebsiteLinkedIn