Description:
- Client is building Energy Intelligence for Services: an AI-driven service that turns existing condition-based maintenance (CBM) analytics into normalized health scores, contextual diagnostics and prioritized action plans for the critical power equipment of data centers, starting with UPS and busway.
- The service is developed and validated on historical, already-diagnosed customer data rather than on live connections. A semantic layer - an asset and relationship model built on Company's existing ontology work - gives the diagnostic agents the context they need: which assets are connected, what depends on what, and where they sit on site.
Key Responsibilities:- Harvest and organise the customer's historical asset, alarm and maintenance data so it can be used for ontology creation.
- Run entity resolution: reconcile the same asset appearing in several systems under several names, and define the identifier strategy (IRIs, named graphs, per-customer and per-site partitioning).
- Build the initial ontology for this dataset on top of the existing client ontology: asset types, power-chain relationships (upstream / downstream, same room, same system) and the links to health-score data.
- Validate the data against the model with SHACL and produce data-quality reports the team can act on.
- Run modelling sessions with service, reliability and CBM experts; turn their knowledge into the model and a shared glossary.
- Treat the ontology and mappings as code: versioned, tested (competency questions as SPARQL tests, SHACL conformance) and run in CI.
- Write the documentation, schema summaries and example queries that ground the diagnostic agents.
- Maintain the competency questions and expected answers used to test the agents on historical incidents.Clearly communicate component status and issues in MS-Office applications e.g. PowerPoint, Excel, Word, Outlook
- Review CAD and 2D drawings for components including GD&T
Mandatory Skills:- 4+ years building knowledge graphs, ontologies or semantic data platforms that were used in production.
- Hands-on RDF/RDFS, OWL 2, SPARQL 1.1 and SHACL.
- Solid Python (rdflib, pySHACL or similar), git and CI.
- Ability to work with domain experts and to explain modelling choices to software engineers.
- Data preparation and entity resolution on messy, multi-source asset data (relational, spreadsheets, JSON).
- Mapping data to RDF (R2RML/RML or equivalent ETL).
Preferred Requirements:- Electrical power-chain or data-center domain knowledge (UPS, busway, switchgear, PDUs) and maintenance vocabulary (failure modes, FMEA, condition monitoring).
- Standard vocabularies: Brick, RealEstateCore / ASHRAE 223, SOSA/SSN, QUDT, ISO 14224, IEC 81346.
- Combining knowledge graphs with LLMs: GraphRAG, text-to-SPARQL, ontology-grounded prompting.
- Property graphs (Neo4j/Cypher), with an informed view of when each approach fits.
- Prote ge , TopBraid or LinkML used at scale.
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