18 hours ago
London, United KingdomSenior
Responsibilities
- Automate and orchestrate machine learning workflows across AWS, Azure, Databricks, and foundation model APIs such as OpenAI.
- Maintain and version model registries and artifact stores for reproducibility and governance.
- Develop CI/CD for ML with automated data validation, model testing, and deployment.
- Implement MLOps solutions using AWS SageMaker, MLflow, and Azure ML, including custom SageMaker pipelines for recommendation systems.
- Build engineering components for RAG systems, including query interpretation, reflection, chunking, embeddings, hybrid retrieval, semantic search, prompt libraries, guardrails, and structured LLM output.
- Design ML pipelines using Elasticsearch, OpenSearch, Solr, vector databases, and graph databases.
- Build evaluation pipelines using offline information-retrieval metrics, LLM quality metrics, and A/B testing.
- Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization.
- Apply current research in generative AI, NLP, and RAG to experiments and production systems.
- Partner with subject-matter experts, Product Managers, Data Scientists, Responsible AI experts, and Operations Engineers.
Requirements
- Extensive experience in ML engineering, MLOps platforms, and shipping ML, search, or GenAI systems to production.
- Strong engineering skills in Python, Java, and/or Scala.
- Experience with statistical analysis, machine learning theory, and natural language processing.
- Hands-on experience with major cloud providers, including AWS, Azure, and/or Google.
- Experience with search, vector, and graph technologies such as Elasticsearch, OpenSearch, Solr, and Neo4j.
- Experience evaluating LLM models.
- Background in scholarly publishing workflows, bibliometrics, or citation graphs.
- Strong understanding of the Data Science Life Cycle, including feature engineering, model training, and evaluation metrics.
- Familiarity with ML frameworks such as PyTorch, TensorFlow, and PySpark.
- Experience with large-scale data processing systems such as Spark.
Benefits
- Flexible working hours.
- Comprehensive pension plan.
- Generous vacation entitlement and sabbatical leave options.
- Maternity, paternity, adoption, and family care leave.
- Internal communities and networks.
- Various employee discounts.
- Recruitment introduction reward.
- Global Employee Assistance Program.
- Wellbeing initiatives, shared parental leave, study assistance, and country-specific benefits.
Categories
About RELX
RELX provides information-based analytics, research content, and decision tools for scientists, legal and risk professionals, insurers, and governments. Through segments including Elsevier (STM), LexisNexis (legal and risk), and RX (exhibitions), it sells subscriptions, data services, and software platforms used in 180+ countries. Headquartered in London, it is a public company listed in London and Amsterdam.