Senior Applied AI Engineer/Scientist – Semantic AI, 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 and Amazon's Central Reliability Maintenance Engineering (C-RME) team is at the heart of that mission, using science and data to drive scalable maintenance best practices across Amazon business units globally.

We are seeking a Senior Applied AI Engineer/Scientist to lead key semantic layer and knowledge intelligence initiatives. This role sits at the intersection of knowledge engineering, ontology design, and applied AI, owning workstreams for the development of semantic foundations and dedicated science approaches that ensure their accuracy, consistency, and explainability in service of agentic and non-agentic AI across RME.

Key job responsibilities

In this role, you will contribute to the success of Central and Field RME teams working with new launches of Amazon buildings, as well as ensure that our Field teams benefit from state-of-the art AI solutions to support Global Operational Excellence.

You will closely work with our team of senior scientists and systems engineers in our knowledge intelligence team, which is leading the full lifecycle of graph-based AI solutions, from customer problem formulation and ontology design to production deployment, enabling network-wide data discovery, decision support, and compliance monitoring.

A core part of your mandate is to lead the semantic modelling and ontological foundation layer that supports both explainability and retrieval capabilities. This foundation feeds into transversal initiatives spanning multiple teams and products involving multiple AI approaches. You will closely work with Senior Applied Scientists owning explainability, causal reasoning, intelligent retrieval and question answering over knowledge graphs.

As a Senior Applied AI Engineer/Scientist, you will:

• lead the semantic layer for agentic AI, including developing and assessing the ontological foundations that enable autonomous workflows and cross-site best practice sharing. You will also disseminate governance and standardization practices that ensure downstream consumers (including retrieval and explainability systems) operate on consistent, well-defined semantics
• design, build, and deploy graph-based AI solutions that combine knowledge graphs, Large Language Models (LLMs), and ML models to extract meaning from large-scale unstructured document collections, enabling data discovery, classification, and governance across RME
• define and own knowledge pipelines that extract, transform, and enrich entity relationships from diverse unstructured and semi-structured sources into production-grade knowledge graphs, ensuring reliability and accuracy of the overall information architecture
• collaborate with fellow senior scientists to design, deploy, and operate graph and vector databases to support retrieval, causal reasoning, and analytics use case as well as ensure contributing scientists maintain versioning, validation state, and provenance for every knowledge graph entry
• collaborate with fellow senior applied scientists to ensure ontology and schema design decisions optimize for queryability, so that question-answering and retrieval systems can leverage the semantic layer with minimal impedance mismatch
• integrate LLMs and ML models into text processing pipelines for classification, embedding generation, document similarity, entity extraction, and semantic analysis, applying rigorous experimentation and evaluation methodology to select the best fit-for purpose approach
• design ontological structures that support explainability, enabling agents and reasoning systems to trace reasoning paths and surface provenance, enabling governance-level transparency for autonomous AI actions
• optimize models and inference pipelines for production constraints including latency, throughput, cost, and infrastructure reliability
• establish best practices and standards for knowledge engineering and applied science processes, elevating the maturity of RME's data, information, and AI capabilities
• mentor and train colleagues on knowledge graph concepts, semantic modelling, and applied AI techniques

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. Basic qualifications

• Experience conveying complex technical concepts to both technical and business audiences
• Experience in machine learning, data mining, information retrieval, statistics or natural language processing, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
• • MSc or PhD in Computer Science, Computational Linguistics, Information Science, or a related quantitative field (or equivalent industry experience)
• • Proven experience developing and deploying production knowledge graphs in real business scenarios at enterprise scale
• • Deep expertise in semantic technologies, modelling languages (RDF, OWL, etc..), and querying languages (SPARQL, Gremlin, Cypher, or other GQLs);
• • Experience with MLOps practices: building production-grade pipelines, CI/CD for ML, containerization, and monitoring of deployed AI solutions
• • Appetite for staying at the forefront of semantic web technologies and graph applications

Preferred qualifications

• Knowledge of AWS services including compute, storage, networking, security, databases, machine learning, and serverless technologies
• • PhD in a relevant field with publications in knowledge representation, NLP, or graph-based AI
• • Familiarity with graph visualization tools and techniques
• • Experience leveraging graph theory (traversals, centrality, community detection) and graph analytics in applied settings
• • Experience designing clean, modular, and testable ML code in a collaborative environment

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