RELX

Senior MLOps Engineer

RELX

full-time

Posted on:

Location Type: Office

Location: Amsterdam • Netherlands

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Job Level

Senior

Tech Stack

AWSAzureCloudElasticSearchJavaNeo4jPySparkPythonPyTorchScalaSparkTensorflow

About the role

  • ML & LLM Engineering, Search and Recommendation Engines
  • 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
  • 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

  • 5+ years in 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
Benefits
  • Comprehensive Pension Plan
  • Home, office, or commuting allowance.
  • Generous vacation entitlement and option for sabbatical leave
  • Maternity, Paternity, Adoption and Family Care leave
  • Flexible working hours
  • Personal Choice budget
  • Internal communities and networks
  • Various employee discounts
  • Recruitment introduction reward
  • Employee Assistance Program (global)

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills
ML EngineeringMLOpsPythonJavaScalastatistical analysismachine learning theorynatural language processingfeature engineeringmodel training
Soft skills
collaborationcommunicationproblem-solvinginterfacing with stakeholders