Senior MLOps Engineer

RELX INC; Greater London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading research organization in Greater London is seeking an experienced ML Engineer to support large-scale research platforms by designing and operating machine learning models and production services. The ideal candidate will automate workflows, manage CI/CD for ML, and collaborate with product managers and data scientists to deliver responsible AI features. Applicants should have over 5 years of experience in ML Engineering, expertise in cloud platforms, and strong programming skills. This role offers an opportunity to innovate and impact the field of research.

Qualifications

• 5+ years in ML Engineering, MLOps platforms, and 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).
• Experience with search, vector, and graph technologies (e.g., Elasticsearch, OpenSearch, Solr, Neo4j).
• Experience 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, and PySpark.
• Experience with large-scale data processing systems, e.g., Spark.

Responsibilities

• Automate and orchestrate machine learning workflows across major cloud and AI platforms.
• 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.
• Design and implement the engineering components of GAR+RAG systems.
• Design and implement ML pipelines that utilize Elasticsearch, OpenSearch, and other technologies.
• Build evaluation pipelines: offline IR metrics and LLM quality metrics.
• Optimize infrastructure costs through monitoring and efficient resource utilization.
• Stay current with the latest GAI research, NLP and RAG.

Job description

About the team

Elsevier’s mission is to help researchers, clinicians, and life sciences professionals advance discovery and improve health outcomes through trusted content, data, and analytics. As the landscape of science and healthcare evolves, we are pioneering intelligent discovery experiences—from Scopus AI and LeapSpace to ClinicalKey AI, PharmaPendium, and next‑generation life sciences platforms. These products leverage retrieval‑augmented generation (RAG), semantic search, and generative AI to make knowledge more discoverable, connected, and actionable across disciplines.

About Role

This role supports Elsevier’s large‑scale research platforms by turning experimental NLP, search, and GenAI models into secure, reliable, and scalable production services. It focuses on ML and LLM engineering across cloud platforms, including building end‑to‑end ML pipelines, MLOps infrastructure, and CI/CD for models used in search, recommendations, and RAG‑based systems. The position involves designing and operating retrieval, ranking, and evaluation pipelines, including IR metrics, LLM quality metrics, and A/B testing, while optimizing cost and performance at scale. You will collaborate closely with product managers, domain experts, data scientists, and operations engineers to deliver high‑quality, responsible AI features over a massive scholarly corpus. The role suits an experienced ML engineer with strong cloud, search, and NLP expertise who wants to work at the intersection of GenAI, research content, and production‑grade systems.

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, and 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, 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.

Required Qualifications

• 5+ years in ML Engineering, MLOps platforms, and 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).
• Experience with search, vector, and graph technologies (e.g., Elasticsearch, OpenSearch, Solr, Neo4j).
• Experience 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, and 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.

We are an equal‑opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.

If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or contact 1‑855‑833‑5120.