Senior MLOps Engineer: GenAI Pipelines & Flexible Hours

RELX International; City Of London; On-site; GBP 90,000 - 130,000; Full time

Benefits offered by this job

Comprehensive pension plan

Generous vacation entitlement and sabb

Maternity, paternity, adoption and fam

Employee Assistance Program (global)

Internal communities and networks

Employee discounts

Job summary

Elsevier is seeking a Senior MLOps Engineer to bridge Data Science and Engineering, turning NLP/IR/GenAI experiments into secure, scalable services across cloud platforms. You will own ML pipelines, model governance, and production readiness while collaborating with cross-functional teams on cutting-edge AI features.

You will contribute to knowledge graphs, RAG, and search systems, driving cost optimisation and best practices in a fast-paced research environment.

Qualifications

• Proven experience shipping ML or GenAI systems to production.
• Strong programming skills in Python and at least one JVM language.
• Hands-on with major cloud platforms (AWS, Azure, GCP).
• Experience with ML tooling and MLOps pipelines for CI/CD.

Responsibilities

• Automate and orchestrate ML workflows across cloud and AI platforms.
• Maintain model registries and artifact stores for reproducibility.
• Develop and manage CI/CD for ML including data validation and model testing.
• Implement ML engineering solutions using SageMaker, MLflow, and Azure ML.
• Build end-to-end SageMaker pipelines for recommendation systems.
• Design GAR+RAG modules and manage prompt libraries and guardrails.
• Work with Elasticsearch/OpenSearch/Solr, vector databases, and graph DBs.
• Evaluate IR metrics and LLM quality metrics; run A/B tests.

Skills

Python

Java/Scala

ML Engineering

Cloud platforms

Tools

AWS SageMaker

MLflow

Azure ML

Elasticsearch/OpenSearch

Solr

Neo4j

Spark

Databricks

Job description

Elsevier is seeking a Senior MLOps Engineer to bridge Data Science and Engineering, turning NLP/IR/GenAI experiments into secure, scalable services across cloud platforms. You will own ML pipelines, model governance, and production readiness while collaborating with cross-functional teams on cutting-edge AI features.

You will contribute to knowledge graphs, RAG, and search systems, driving cost optimisation and best practices in a fast-paced research environment.