# Senior MLOps Engineer at RELX

RELX is hiring a Senior MLOps Engineer. Level rates it Builds AI ●●●●; you can [apply on RELX](https://jobsbylevel.com/go/f0e46ec8-2088-4fc8-b532-d0d6b24e1173?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. London Wall.

## Details

- Company: [RELX](https://jobsbylevel.com/companies/relx)
- AI level: AI Level 4 (score 90 out of 100)
- Location: London Wall
- Posted: October 23, 2026
- Apply: https://jobsbylevel.com/go/f0e46ec8-2088-4fc8-b532-d0d6b24e1173?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-mlops-engineer-at-relx-70ab41

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## Description

Senior MLOps Engineer Are you passionate about building scalable, reliable machine learning platforms that bring AI models into production? Do you thrive on automating ML workflows, optimising model performance, and creating robust MLOps solutions at scale? About the team: Join the team that powers Elsevier’s research platforms—Scopus/Scopus AI, ScienceDirect/ScienceDirect AI, and journal submission & peer review workflows. About the role: You will bridge Data Science and Engineering to turn experimental NLP/IR/GenAI models into secure, reliable, and scalable services. Our systems operate over one of the world’s largest scholarly corpora, so you’ll work on AI-based features (GenAI, Agentic AI, RAG, etc.), search/ranking quality, and knowledge graph-aware retrieval while enforcing content rights and editorial confidentiality. As a Senior MLOps Engineer, you should be able to define the scope of a project with support of managers and execute that project independently. Individuals in this role can also support the development and training of junior staff. Senior Data Scientist I should be self-sufficient in executing basic methods, and work within their teams to execute increasingly sophisticated approaches to deliver outcomes. They should also support the development of best practices. Key Responsibilities: · Automate and orchestrate machine learning workflows across major cloud and AI platforms (AWS, Azure, Databricks, and foundation model APIs such as OpenAI) · Maintain and version model registries and artifact stores to ensure reproducibility and governance · Develop and manage CI/CD for ML, including automated data validation, model testing, and deployment. · Implement ML Engineering solutions using popular MLOps platforms such as AWS SageMaker, MLflow, Azure ML. · End-to-end custom SageMaker pipelines for recommendation systems. · Design and implement the engineering components of GAR+RAG systems (e.g., query interpretation and reflection, chunking, embeddings, hybrid retrieval, semantic search), manage prompt libraries, guardrails and structured output for LLMs hosted on Bedrock/SageMaker or self-hosted · Design and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs · Build evaluation pipelines: offline IR metrics (e.g., NDCG, MAP, MRR), LLM quality metrics (e.g., faithfulness, grounding), and A/B testing. · Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization · Stay current with the latest GAI research, NLP , and RAG, and apply the state-of-the-art in our experiments and systems Collaboration · Partner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutions · Collaborate and interface with Operations Engineers who deploy and run production infrastructure. Requirements: · Extensive experience ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production. · Strong Python, Java, and/or Scala engineering · Experience with statistical analysis, machine learning theory and natural language processing · Hands-on‑ experience with major cloud vendor solutions (AWS, Azure and/or Google) · Search/vector/graph technologies (e.g., Elasticsearch / OpenSearch / Solr/ Neo4j). · Experience in evaluating LLM models · Background with scholarly publishing workflows, bibliometrics, or citation graphs · A strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metrics · Familiarity with ML frameworks, e.g., PyTorch, TensorFlow, PySpark · Experience with large-scale data processing systems, e.g., Spark Why Join Us? Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance your career and make a significant impact, we encourage you to apply. Work in a way that works for you We promote a healthy work/life balance

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