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ACL Digital is seeking a specialist to lead ontology and knowledge-graph initiatives for a locomotive enterprise. You will gather requirements, define the semantic layer, and drive platform selections to underpin BI, data products, and AI agents at scale.
You will collaborate with data architecture and business stakeholders to ensure alignment with data layers, prepare RFPs for pre-studies, and manage vendor onboarding for the project’s early phases.
A Leading Locomotive company is at an early stage of building an enterprise ontology capability that will underpin trusted AI, consistent business meaning, and cross-domain intelligence. This role will help select the right ontology platform and lay the foundation to move the organization from fragmented semantic definitions and dashboard-centric logic to a governed enterprise knowledge graph layer supporting BI, data products, and AI agents at scale.
Lead requirement gathering and use-case listing for the Ontology & Knowledge Graph platform
Prepare the requirement document to initiate the RFP for the pre-study phase
Partner closely with the Data Architect and business stakeholders to align ontology/knowledge graph scope with the Data layer
Evaluate candidate ontology/knowledge-graph platforms (e.g. iSenS and others in scope)
Define the enterprise semantic/meaning layer that will support BI, data products, and AI agents
Support vendor selection and onboarding for the pre-study execution
Develop the target architecture and support G2 approval preparation (Architecture, Security, Finance)
Hands-on experience with knowledge graph platforms (e.g. Neo4j, Stardog, GraphDB, Amazon Neptune)
Ability to translate business domain knowledge into formal ontological models
Experience authoring RFP/requirement documents and evaluating vendors
Experience partnering with enterprise data architecture teams
Familiarity with GenAI/agentic AI systems consuming knowledge graphs
Domain ontology experience in rail, industrial, or engineering sectors
Bachelor's/master’s in computer science, Data Science, Knowledge Engineering, or related field.