Senior MLOps Engineer: Scale GenAI & RAG Pipelines

Elsevier; Greater London; On-site; GBP 90,000 - 140,000; Full time

Benefits offered by this job

Pension plan

Vacation leave

Sabbatical leave

Employee discounts

Assistance program

Job summary

Elsevier is seeking a Senior MLOps Engineer to bridge Data Science and Engineering, turning experimental NLP/IR/GenAI models into secure, scalable services running on a large scholarly corpus.

You will own end-to-end ML pipelines, CI/CD for ML, and work with AWS, Azure, Databricks, and OpenAI APIs, ensuring reproducibility and governance. Collaborate with scientists, product managers, and Responsible AI experts to deliver production-ready AI features for Scopus and ScienceDirect workflows.

Qualifications

• Experience in ML engineering and MLOps platforms shipping ML or search/GenAI systems to production.
• Familiarity with ML frameworks (PyTorch, TensorFlow, PySpark).
• Hands-on with major cloud vendors (AWS, Azure and/or Google).

Responsibilities

• Automate and orchestrate ML workflows across cloud platforms.
• Maintain and version model registries and artifact stores for reproducibility and governance.
• Develop and manage CI/CD pipelines for ML, including data validation and model testing.
• Implement ML engineering solutions using platforms like AWS SageMaker, MLflow, Azure ML.
• Build end-to-end SageMaker pipelines for recommendation systems.
• Design components of GAR+RAG systems and manage prompt libraries and guardrails.
• Design ML pipelines using Elasticsearch/OpenSearch/Solr, vector DBs and graph DBs.
• Create evaluation pipelines with offline IR metrics and LLM quality metrics.
• Optimize infrastructure costs through monitoring and scaling strategies.

Skills

Python

Java/Scala

Cloud platforms

AWS

Azure

Google Cloud

Tools

SageMaker

MLflow

Azure ML

OpenSearch

Elasticsearch

Job description

Elsevier is seeking a Senior MLOps Engineer to bridge Data Science and Engineering, turning experimental NLP/IR/GenAI models into secure, scalable services running on a large scholarly corpus.

You will own end-to-end ML pipelines, CI/CD for ML, and work with AWS, Azure, Databricks, and OpenAI APIs, ensuring reproducibility and governance. Collaborate with scientists, product managers, and Responsible AI experts to deliver production-ready AI features for Scopus and ScienceDirect workflows.