Senior MLOps Engineer

Job Description Senior MLops Location: Amsterdam About our Team Data Science Life Sciences is a diverse team focusing on GenAI, ML, NLP. We mainly develop best-in-class enrichment pipelines for Elsevier’s life science .com products such as Reaxys, Embase and Pharmapendium. About Role: Join the team that powers Elsevier’s Data Scientists at Corporate Markets in the domain of Life Sciences. You will bridge Data Science and Engineering to turn experimental NLP/IR/GenAI models into secure, reliable, and scalable services. Our work empowers R&D within Chemistry and Biology domain, to support that 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 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-end custom Sagemaker pipelines for recommendation systemsDesign 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-hostedDesign and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs Build evaluation pipelines: offline IR metrics (NDCG, MAP, MRR), LLM quality metrics (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 Data Scientists, Engineers, Subject Matter Experts, Product Managers, and Responsible AI experts to support translate business problems into cutting edge data science solutions Collaborate and interface with Operations Engineers who deploy and run production infrastructure. Required Qualifications 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 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. 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.