Semantic Platform Engineer

Capgemini; Boston (MA); On-site; USD 57,000 - 90,000; Full time

Benefits offered by this job

Medical, dental, vision

Retirement benefits

Job summary

Capgemini is building Energy Intelligence for Services, an AI-driven platform turning condition-based maintenance analytics into health scores and prioritized action plans for critical data-center power equipment, starting with UPS and busway.

The role focuses on deploying the ontology engine, building data pipelines in Azure/Databricks, keeping deployments portable with containers and IaC, and documenting queries and schemas for diagnostic agents.

Qualifications

• 4+ years building knowledge graphs, ontologies or semantic data platforms in production.
• Hands-on RDF/RDFS, OWL 2, SPARQL 1.1 and SHACL.
• Solid Python (rdflib, pySHACL) and CI/CD experience.
• Deploying and operating an RDF triple store or reasoner in production.
• Azure: containers, infrastructure as code, CI/CD; Databricks experience.

Responsibilities

• Deploy the ontology engine and a shared semantic repository with partner teams.
• Build pipelines that load data into the repository on Azure and Databricks.
• Keep deployments portable with containers and infrastructure as code.
• Expose graph to diagnostic agents via read-only endpoints and measure performance.
• Treat ontology/mappings as code with versioning, tests and CI runs.
• Write documentation, schema summaries and example queries for agents.
• Maintain competency questions and answers used to test agents on historical incidents.

Skills

Knowledge graphs

Ontologies

Semantic data platforms

Python

Git

CI/CD

Databricks

Azure

Tools

GraphDB

Stardog

RDFox

Apache Jena/Fuseki

Job 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.

Protege, TopBraid or LinkML used at scale.

The pay range that the employer in good faith reasonably expects to pay for this position is $41.61/hour - $65.01/hour. Our benefits include medical, dental, vision and retirement benefits. Applications will be accepted on an ongoing basis.

Tundra Technical Solutions is among North America’s leading providers of Staffing and Consulting Services. Our success and our clients’ success are built on a foundation of service excellence. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Unincorporated LA County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: client provided property, including hardware (both of which may include data) entrusted to you from theft, loss or damage; return all portable client computer hardware in your possession (including the data contained therein) upon completion of the assignment, and; maintain the confidentiality of client proprietary, confidential, or non-public information. In addition, job duties require access to secure and protected client information technology systems and related data security obligations.