Senior MLOps Engineer

RELX International

City Of London

On-site

GBP 90,000 - 130,000

Full time

3 days ago

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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

Senior MLOps Engineer

Are you passionate about building scalable, reliable machine learning platforms that bring AI models into production?

Do you thrive on automating ML workflows, optimising model performance, and creating robust MLOps solutions at scale?

About the team

Join the team that powers Elsevier's research platforms-Scopus/Scopus AI, ScienceDirect/ScienceDirect AI, and journal submission & peer review workflows.

About the role

You will bridge Data Science and Engineering to turn experimental NLP/IR/GenAI models into secure, reliable, and scalable services. Our systems operate over one of the world's largest scholarly corpora, so you'll work on AI-based features (GenAI, Agentic AI, RAG, etc.), search/ranking quality, and knowledge graph-aware retrieval while enforcing content rights and editorial confidentiality.

As a Senior MLOps Engineer, you should be able to define the scope of a project with support of managers and execute that project independently. Individuals in this role can also support the development and training of junior staff. Senior Data Scientist I should be self-sufficient in executing basic methods, and work within their teams to execute increasingly sophisticated approaches to deliver outcomes. They should also support the development of best practices.

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, 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

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.

Requirements

Extensive experience 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

Why Join Us?

Join our team and contribute to a culture of innovation, collaboration, and excellence.

Work in a way that works for you

We promote a healthy work/life balance across the organization. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.