Senior MLOps Engineer

Join the team that powers Elsevier’s research platforms—Scopus/Scopus AI, Science Direct/Science Direct 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.

• 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 Sage Maker, MLflow, Azure ML
• End‑to‑end custom Sage Maker 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/Sage Maker or self‑hosted
• Design and implement ML pipelines that utilize Elasticsearch/Open Search/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

• 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 / Open Search / 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, Tensor Flow, Py Spark
• Experience with large scale data processing systems, e.g., Spark

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 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

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…