Senior MLOps

Elsevier; City Of London; On-site; GBP 85,000 - 125,000; Full time

Job summary

Elsevier is seeking an ML Engineer to bridge Data Science and Engineering for scalable NLP/IR/GenAI services across its research platforms. You will work on AI-based features, search quality, and knowledge-graph aware retrieval within a secure, editorially controlled environment.

You will collaborate with cross-functional teams to deliver end-to-end ML pipelines, governance, and cost-efficient infrastructure while staying at the forefront of AI and machine learning research.

Qualifications

• 3–5+ years in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.
• Strong Python, Java, and/or Scala engineering
• Experience with statistical analysis, ML theory and natural language processing
• Hands-on experience with major cloud vendor solutions (AWS, Azure and/or Google)
• Search/vector/graph technologies (Elasticsearch/OpenSearch/Solr/Neo4j)
• Experience in evaluating LLM models
• Background with scholarly publishing workflows, bibliometrics, or citation graphs
• 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

Responsibilities

• Automate and orchestrate ML workflows across AWS, Azure, Databricks, and LLM APIs.
• 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 MLOps platforms such as AWS SageMaker, MLflow, Azure ML.
• End-to-end custom SageMaker pipelines for recommendation systems.
• Design and implement GAR+RAG components (query interpretation, embeddings, hybrid retrieval, semantic search); manage prompts.
• Design and implement ML pipelines using Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs
• Build evaluation pipelines: offline IR metrics (NDCG, MAP, MRR), LLM quality metrics, A/B testing.
• Optimize infrastructure costs via monitoring, scaling and resource utilization
• Stay current with GAI research, NLP and RAG; apply state-of-the-art in experiments

Skills

Python

Java/Scala

NLP

AWS/Azure/Google Cloud

Elasticsearch/OpenSearch/Solr/Neo4j

LLM evaluation

PyTorch/TensorFlow

Spark

Tools

SageMaker

MLflow

Azure ML

Job description

Are you passionate about building scalable AI and machine learning systems that power world-leading research and healthcare platforms?

Do you enjoy turning cutting-edge NLP, search, recommendation, and Generative AI innovations into reliable, secure, and production-ready solutions that create real-world impact?

About our Team

Our global team support products education electronic health records that introduce students to digital charting and prepare them to document care in today’s modern clinical environment. We have a very stable product that we’ve worked to get to and strive to maintain. Our team values trust, respect, collaboration, agility, and quality.

About the Role

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

Key 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.

Required Qualifications

• 3–5+ years in 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 world.

Primary Location Base Pay Range: NLD Amsterdam (Radarweg) €53,800 - €89,900.

This role is covered by the Collective Labor Agreement Publishing Industry.

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