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 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 (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.
Primary Location Base Pay Range: NLD Amsterdam (Radarweg) €53,800 - €89,900. This role is covered by the Collective Labor Agreement Publishing Industry.We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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Skills Required
- 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 Cloud)
- Experience with search, vector, and graph technologies (Elasticsearch/OpenSearch/Solr/Neo4j or similar)
- Experience evaluating LLM models and LLM-based systems (RAG/GAR/GenAI)
- Background with scholarly publishing workflows, bibliometrics, or citation graphs
- Strong understanding of the data science lifecycle including feature engineering, model training, and evaluation metrics
- Familiarity with ML frameworks such as PyTorch, TensorFlow, and PySpark
- Experience with large-scale data processing systems (e.g., Spark, Databricks)
Elsevier Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Elsevier and has not been reviewed or approved by Elsevier.
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Leave & Time Off Breadth — Feedback suggests paid time off spans vacation, holidays, sick days, bereavement, military leave, and volunteer time. Family-related leave options are also emphasized as part of the package.
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Healthcare Strength — Feedback suggests medical, dental, vision, life insurance, wellness initiatives, and an EAP form a comprehensive health offering. Gym support and wellbeing hubs reinforce an ongoing health and wellness focus.
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Retirement Support — Feedback suggests retirement programs include a 401(k)/retirement plan and pension options, with long-term savings vehicles such as an employee stock purchase plan also available. These elements contribute to a sense of financial security beyond base pay.
Elsevier Insights
What We Do
Elsevier is a world-leading provider of information solutions that enhance the performance of science, health, and technology professionals, empowering them to make better decisions, and deliver better care. Because informed decisions lead to better outcomes, Elsevier is a leader in information and analytics for customers across the global research and health ecosystems. Elsevier helps researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. We do this by facilitating insights and critical decision-making for customers across the global research and health ecosystems.
