Senior Knowledge Graph Engineer & Semantic Architect, Central Reliability Maintenance Engineering

At Amazon we believe that Every Day is still Day One! We’re working to be the most customer-centric company on earth. Are you passionate about pushing the boundaries of semantic technologies and knowledge graphs? Do you want to shape the future of data-driven decision-making at one of the world's most innovative companies? Amazon's Central Reliability Maintenance Engineering team (C-RME) is seeking a Senior Knowledge Graph Engineer & Semantic Architect to lead our next-generation information architecture (IA) initiatives.

Key job responsibilities
In this role, you will contribute to the success of Central and Field RME teams working with New Technologies Introduction (NTI) in Amazon buildings, as well as ensure that our Building eXcellence Management (BXM) teams benefit from state-of-the art solutions for Quality & Compliance monitoring purposes.
You will architect innovative solutions that integrate knowledge graphs with advanced AI technologies, including AI text extraction and Large Language models (LLMs). In addition, you will contribute to longer term multi-functional programs involving multiple AI technologies.
As a Senior Knowledge Graph Engineer, you will:
- Lead the evolution of our knowledge graph and semantic data engineering capabilities, driving innovation in our graph-based architectures for 2025-2027 initiatives and beyond;
- Collaborate with business stakeholders to translate business requirements into graph-based solutions which ensure the decision-making needs of our customers are met;
- Design and implement scalable, high-performance knowledge pipelines (from data/information ingestion to storage, and retrieval) while ensuring reliability of our overall information architecture and data obtained from graph-based services;
- Lead the development and evolution of sophisticated ontologies and knowledge models
- Design and implement ontology mapping and alignment strategies to facilitate integration and interoperability across diverse systems as well as ensure consistent knowledge representation across RME;
- Explore new approaches to representing multi-modal data relationships, enabling efficient querying and reasoning over diverse data types;
- Establish best practices and standards for knowledge engineering processes, to elevate the maturity of RME’s data and information capabilities
- Collaborate with other RME teams to define and implement a robust semantic layer, providing a common understanding across the organization while enabling new AI capabilities;
- Develop and implement rigorous testing processes to ensure accuracy, reliability and security of knowledge-graph powered services and applications;
- Mentor and train colleagues on knowledge graph concepts, implementation and key benefits for identified and new business cases.

About the team
The Amazon Reliability and Maintenance Engineering (RME) team maintains and optimizes technologies ranging from large, modern, purpose-built warehouses utilizing robotics and high-volume conveyance all the way through the value chain to small, high-speed warehouses placed as close to our customers as possible. Central Reliability Maintenance Engineering (RME) uses science and data to drive scalable maintenance best practices across Amazon business units globally. We do this to meet our customer promise, reduce costs, and support the Climate Pledge. The Decision Science & Technology (DST) team in RME uses machine learning to develop predictive models for spare parts, cycle-based maintenance, predictive maintenance, energy consumption, and refrigeration health status monitoring programs. - In-depth understanding of data and information architecture key components, tools, and frameworks
- Deep expertise in semantic technologies, modelling languages (RDF, OWL, etc..), and querying languages (Cypher, SPARQL and other GQLs);
- Strong background in graph databases, triple/quad stores; and a sound understanding of other databases (NoSQL/non-relational);
- Proven experience architecting and scaling enterprise-grade ontologies and knowledge graphs;
- Ability to communicate technical concepts to both technical and non-technical audiences
- Appetite for staying at the forefront of semantic web technologies and graph applications- Familiarity with AWS Technologies, including but not limited to Amazon Neptune
- Familiarity with graph visualization tools and techniques
- Experience working with products integrating LLMs with knowledge graphs
- Experience working with products leveraging graph theory (such as graph traversals) and graph analytics
- Familiarity with machine learning concepts and formal logic


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