Semantic Graph Engineer for AI-Driven Procurement

Chain IQ

Lisboa

In loco

EUR 70.000 - 100.000

Tempo pieno

48 ore fa

Candidati tra i primi

Generatore di candidature

Una candidatura fatta su misura per questo lavoro — un curriculum e una lettera di presentazione personalizzati, perfettamente in linea con l'annuncio.

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Descrizione del lavoro

Chain IQ is building the semantic backbone for its AI-native procurement platform. This role defines and evolves ontologies, knowledge graphs, and context graphs that enable retrieval, reasoning, agent orchestration, workflow automation, and decision support.

You will collaborate with Data Architecture, Data Engineering, Product, AI, Domain, and Governance teams to ensure graph structures are feasible, governed, and useful across our global procurement operations.

Competenze

Experience with semantic modelling, ontology design, and knowledge graphs.
Strong understanding of entities, relationships, events, and business context.
Ability to translate complex procurement concepts into reusable semantic structures.
Familiarity with graph databases and RDF/property graphs.
Understanding of entity resolution, provenance, and metadata management.
Ability to collaborate with data engineers, architects, product teams, and AI engineers.

Mansioni

Define and maintain procurement ontologies covering entities, attributes, relationships, events, states, and business concepts.
Own the semantic model for core domains such as suppliers, contracts, obligations, sourcing events, categories, risks, savings, and operational workflows.
Design and evolve procurement knowledge graphs representing entities, relationships, dependencies, events, and business context.
Define context graph patterns used to assemble relevant information for users, workflows, retrieval systems, and AI agents.
Model temporal relationships, lifecycle states, provenance, hierarchy, ownership, and dependency structures explicitly.
Define graph construction patterns linking structured data, documents, communications, workflow events, and external signals to canonical entities.
Establish approaches for entity resolution, supplier normalization, deduplication, identity management, and relationship confidence.
Define validation rules and quality expectations for ontology and graph structures.
Ensure semantic and graph models support retrieval, reasoning, recommendations, workflow automation, and agentic execution.
Collaborate with Data Architecture, Data Engineering, Product, AI, Domain, and Governance teams to ensure graph structures are technically feasible, governed, and useful.