Semantic Platform Engineer

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:

• Deploy the reused Client ontology engine and a single semantic repository shared by the partner teams.
• Build the pipelines that load the resolved data into the repository on Azure, alongside the existing CBM analytics in Databricks.
• Keep the deployment portable (containers, infrastructure as code) so it can later move to another cloud without redesign.
• Expose the graph to the diagnostic agents through documented, read-only query endpoints, and measure query performance.
• 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.

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.
• Deploying and operating an RDF triple store or reasoner (for example GraphDB, Stardog, RDFox, Apache Jena/Fuseki).
• Azure: containers, infrastructure as code, CI/CD; experience working with Databricks.

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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