Senior MLOps Engineer

Elsevier; City Of London; On-site; GBP 100,000 - 140,000; Full time

Job summary

Elsevier is seeking a senior data science and engineering professional to bridge ML/LLM engineering with scalable services for Scopus, ScienceDirect, and manuscript workflows. You will implement GenAI features, RAG and knowledge-graph–aware retrieval while upholding content rights and editorial confidentiality.

Lead ML pipelines, orchestration across AWS/Azure/Databricks, and production-grade SageMaker/MLflow workflows.

Qualifications

• Extensive experience ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.
• Strong Python, Java, or Scala engineering and software design skills.
• Hands-on experience with major cloud vendors (AWS/Azure/Google) and ML tooling.

Responsibilities

• ML & LLM Engineering, Search and Recommendation Engines.
• Automate and orchestrate ML workflows across cloud platforms and foundation model APIs.
• Maintain model registries, artifact stores, and governance for reproducibility.
• Develop and manage CI/CD for ML, including data validation and deployment.
• Implement ML pipelines using AWS SageMaker, MLflow, Azure ML; build SageMaker pipelines for recommendations.
• Design GAR+RAG components, prompt libraries, guardrails, and structured outputs for hosted LLMs.
• Develop pipelines with Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs; run offline IR metrics and A/B tests.
• Optimize infrastructure costs and stay current with GAI/NLP/RAG research.

Skills

Python/Java/Scala

ML & MLOps

NLP/IR

Cloud platforms

Tools

Elasticsearch/OpenSearch/Solr

Neo4j

PyTorch/TensorFlow

Spark

Job description

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.

Responsibilities

• ML & LLM Engineering, Search and Recommendation Engines
• 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

Work in a Way That Works for You

We promote a healthy work/life balance across the organisation. 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.

Working Pattern

Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive

About the Business

A global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world's grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better worl

Benefits

We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits.

Equal Opportunity Statement

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,

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