Lead Ontology & Knowledge Engineer (Remote)

RTX

41d • Remote

About The Position

Pratt & Whitney is working to once again transform the future of flight - designing, building and servicing engines unlike any the world has ever seen. And because transformation begins from within, we’re seeking the people to drive it. So, calling all curious. Come ready to explore and you’ll find a place where your talent takes flight—beyond the borders of title, a country or your comfort zone. Bring your passion and commitment and we’ll welcome you into a tight-knit team that takes our mission personally. Channel your drive to make a difference into shaping an organization and an industry that’s evolving fast to the future. At Pratt & Whitney, the difference you make is on display every day. Just look up. Are you ready to go beyond? Pratt & Whitney digital technology has an exciting new opportunity for a Lead Ontology & Knowledge Engineer. What You Will Do: You aren't just building a database; you are teaching machines how to think about our business. You are the architect of the logic that powers our digital workforce. As the Lead Ontology & Knowledge Engineer, you will bridge the gap between structured/unstructured enterprise data and our LLM-powered agents. You will design, implement, and maintain the ontologies that serve as the "ground truth" for Pratt and Whitney. You will move us beyond simple RAG (Retrieval-Augmented Generation) toward GraphRAG, enabling our agents to perform multi-hop reasoning and causal analysis. The Mission We are building the central nervous system for AI at Pratt & Whitney. Our goal is not just to manage data, but to deploy autonomous AI agents capable of complex reasoning, decision support, and automation. To achieve this, we need a Lead Ontology & Knowledge Engineer to construct the semantic layer, the Knowledge Graph, that gives our agents the context they need to understand the relationship between a specific engine part, a maintenance schedule, and a supply chain constraint.

Requirements

• Bachelor’s degree in Computer Science, Information Science, Mathematics, or related field with 10+ years of relevant experience; OR a Master's degree with 8+ years of relevant experience; OR a PhD in Computer Science, Information Science, Mathematics, or related field with 5+ years of relevant experience.
• Experience: 5+ years in Ontology Engineering, Knowledge Representation, or Data Modeling.
• Proficiency in Graph Query Languages (SPARQL, Cypher, Gremlin).
• Strong coding skills in Python (essential for our AI stack).
• Experience with semantic web standards (RDF, OWL, SKOS, SHACL).
• AI Integration: Experience working with LLMs, Vector Databases, and RAG architectures.
• U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.

Nice To Haves

• Domain Knowledge: Experience in manufacturing, aerospace, or complex supply chain environments is a strong plus.

Responsibilities

• Ontology & Schema Design Develop and maintain enterprise-grade ontologies (OWL/RDF) that model the complex domain of aerospace engineering, manufacturing, and supply chain. Define the semantic schema for AI Agents, ensuring they share a common vocabulary when communicating across different business units (e.g., ensuring a "Task" in Engineering means the same thing to an agent in Operations).
• Knowledge Graph Implementation Lead the architecture and deployment of our Enterprise Knowledge Graph Build data pipelines (Python) to ingest data from legacy ERP systems, PLM software, and unstructured documents into the graph. Implement Entity Resolution and linking strategies to unify disparate data points across the organization.
• Context Engineering for AI Agents Collaborate with AI Engineers to design "Context Windows" for LLMs. You will determine what graph data needs to be injected into a prompt to maximize agent accuracy. Work on Causal Inference models: Structure data to help agents distinguish between correlation and causation (e.g., Did the maintenance delay cause the part failure, or did the part failure cause the delay?).
• Governance & Standards Establish semantic standards and data governance policies for the AI ecosystem. Evangelize the use of Knowledge Graphs within Pratt & Whitney, training other developers on graph-based thinking.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees