Senior MLOps

<p></p><p><span><b>Job Title: ML Engineer – Research Products for Academic, Government and Scientific, Technical and Medical Journals <br/>Location: Amsterdam (Hybrid) <br/>Department: Data Science A&amp;G and STMJ <br/>Reports to: Manager, Data Science</b> </span></p><p></p><p><b><span>About the Role</span></b></p><p></p><p><span>Join the team that powers Elsevier’s research <span>platforms—Scopus/Scopus</span> AI, <span>ScienceDirect/ScienceDirect</span> AI, and journal submission &amp; 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.</span></p><p></p><p><b><span>About our Team</span></b></p><p></p><p><span>This team’s mission is transforming data into actionable insights. This role is perfect for those who thrive in a dynamic environment and are passionate about leveraging their data expertise to influence decision-making in the research domain.</span></p><p></p><p></p><p><b><span>Key <span>Responsibilities </span></span></b></p><p></p><p><span><b>ML &amp; LLM Engineering, Search and Recommendation Engines</b> </span></p><ul><li><span>Automate and orchestrate machine learning workflows across major cloud and AI platforms (AWS, Azure, Databricks, and foundation model APIs such as OpenAI) </span></li><li><span>Maintain and version model registries and artifact stores to ensure reproducibility and governance </span></li><li><span>Develop and manage  CI/CD for ML, including automated data validation, model testing, and deployment. </span></li><li><span>Implement ML Engineering solutions using popular MLOps platforms such as AWS SageMaker, MLflow, Azure ML. </span></li><li><span>End-end custom Sagemaker pipelines for recommendation systems </span></li><li><span>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  </span></li><li><span>Design and implement ML pipelines that utilize <span>Elasticsearch/OpenSearch/Solr,</span> vector DBs, and graph DBs  </span></li><li><span>Build evaluation pipelines: offline IR metrics (NDCG, MAP, MRR), LLM quality metrics (faithfulness, grounding), and A/B testing. </span></li><li><span>Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization </span></li><li><span>Stay current with the latest GAI research, NLP and RAG and apply the state-of-the-art in our experiments and systems </span></li></ul><p></p><p><b><span>Collaboration </span></b></p><ul><li><span>Partner with Subject-Matter Experts, Product Managers,  Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutions </span></li><li><span>Collaborate and interface with Operations Engineers who deploy and run production <span>infrastructure. </span></span></li></ul><p></p><p><b><span>Requirements</span></b></p><ul><li><span>4+ years in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production. </span></li><li><span>Strong Python; Java, and/or Scala experience will be considered a plus </span></li><li><span>Experience with statistical analysis, machine learning theory and natural language processing </span></li><li><span>Hands-on experience with major cloud vendor solutions  (AWS, Azure and/or Google) </span></li><li><span>Search/vector/graph technologies (e.g., <span>Elasticsearch/OpenSearch/Solr//Neo4j). </span></span></li><li><span>Experience in evaluating LLM models </span></li><li><span>Background with scholarly publishing workflows, bibliometrics, or citation graphs </span></li><li><span>A strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metrics </span></li><li><span>Familiarity with ML frameworks, e.g., PyTorch, TensorFlow, PySpark </span></li><li><span>Experience with large scale data processing systems, e.g., Spark </span></li></ul><p></p><p></p><p><b><span>Work in a way that works for you</span></b></p><p></p><p><span>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.</span></p><p></p><p><b><span>Working for you</span></b></p><p></p><p><span>We know that your wellbeing and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:</span></p><ul><li><span>Dut