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Senior Applied AI Engineer/Scientist – Semantic AI, Central Reliability Maintenance Engineering

Amazon· Central Reliability Maintenance Engineering (C-RME)
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Hybrid (Luxembourg (LUX))Full-timePosted 1mo ago

About the role

Senior Applied AI Engineer/Scientist focusing on semantic layer, knowledge graphs, and applied AI to support reliability and maintenance across Amazon operations. Leads semantic modelling, ontology design, and production-grade graph-based AI solutions with explainability and retrieval capabilities across RME.

Responsibilities

  • lead the semantic layer for agentic AI, including developing and assessing the ontological foundations that enable autonomous workflows and cross-site best practice sharing
  • 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 cases as well as ensure provenance for every knowledge graph entry
  • collaborate with fellow senior applied scientists to ensure ontology and schema design decisions optimize for queryability for QA and retrieval systems
  • integrate LLMs and ML models into text processing pipelines for classification, embedding generation, document similarity, entity extraction, and semantic analysis
  • design ontological structures that support explainability and governance-level transparency for autonomous AI actions
  • optimize models and inference pipelines for production constraints including latency, throughput, cost, and reliability
  • establish best practices and standards for knowledge engineering and applied science processes
  • mentor and train colleagues on knowledge graph concepts, semantic modelling, and applied AI techniques

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

Skills

Required:Semantic modellingKnowledge graphsGraph databases and vector databasesLLMs and NLPMLOpsOntology and schema design
Nice to have:Explainability and provenance

Benefits

  • Equal opportunities employer
  • Diversity and inclusive experiences
  • Accommodations available during application and onboarding process
Last updated Oct 25, 2026. Interested? Apply on the company site.

About Amazon

Amazon.com, Inc. or its affiliates

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