US_East | Platform Engineer - DevOps Specialist _L3

The client is developing Energy Intelligence for Services, an AI-driven solution designed to transform existing Condition-Based Maintenance (CBM) analytics into normalized equipment health scores, contextual diagnostics, and prioritized action plans for critical power infrastructure in data centers, initially focusing on Uninterruptible Power Supply (UPS) systems and busways. The solution will be developed and validated using historical, previously diagnosed customer data rather than live system connections. A semantic layer, built on the company’s existing ontology framework, will provide diagnostic AI agents with essential asset and relationship context, including equipment connectivity, dependencies, and physical locations within facilities., * Deploy and configure the client’s existing ontology engine and establish a centralized semantic repository shared across partner teams.

• Develop data pipelines to load resolved data into the semantic repository on Microsoft Azure, integrating with existing CBM analytics in Databricks.
• Design and maintain a portable deployment architecture using containerization and Infrastructure as Code (IaC), enabling future migration to other cloud platforms without major redesign.
• Expose knowledge graph data to diagnostic AI agents through well-documented, read-only query endpoints.
• Monitor, measure, and optimize graph query performance.
• Manage ontologies and semantic mappings as code, incorporating version control, automated testing, and Continuous Integration (CI).
• Implement competency questions as SPARQL tests and validate ontology conformance using SHACL.
• Create comprehensive technical documentation, schema summaries, and sample queries to support ontology-grounded diagnostic agents.
• Maintain competency questions and expected results for testing AI agents against historical maintenance incidents.
• Collaborate with domain experts, data engineers, and software development teams to ensure accurate semantic modeling and system integration.

• Minimum 4+ years of experience developing and implementing production-grade knowledge graphs, ontologies, or semantic data platforms.
• Strong hands-on expertise in semantic web technologies, including:
• RDF/RDFS
• OWL 2
• SPARQL 1.1
• SHACL
• Strong Python programming skills, including experience with libraries such as rdflib, pySHACL, or equivalent tools.
• Proficiency with Git, version control, and Continuous Integration (CI) practices.
• Experience deploying, managing, and operating RDF triple stores or reasoning engines, such as:
• GraphDB
• Stardog
• RDFox
• Apache Jena/Fuseki
• Hands-on experience with Microsoft Azure, including:
• Containerized application deployments
• Infrastructure as Code (IaC)
• CI/CD pipelines
• Databricks integration
• Ability to collaborate with subject matter experts and clearly communicate ontology modeling decisions to software engineering teams., * Knowledge of electrical power distribution systems and data center infrastructure, including UPS, busways, switchgear, and Power Distribution Units (PDUs).
• Familiarity with maintenance engineering concepts, including failure modes, Failure Mode and Effects Analysis (FMEA), and condition monitoring.
• Experience with industry-standard semantic vocabularies and frameworks, such as:
• Brick Schema
• RealEstateCore / ASHRAE 223
• SOSA/SSN, * Experience integrating knowledge graphs with Large Language Models (LLMs), including GraphRAG, text-to-SPARQL, and ontology-grounded prompting.
• Knowledge of property graph technologies, such as Neo4j and Cypher, and an understanding of when to use RDF-based versus property graph approaches.
• Experience using ontology modeling tools such as Protégé, TopBraid, or LinkML in large-scale environments.