Senior MLops Engineer

Elsevier; Amsterdam; On-site; EUR 110,000 - 140,000; Full time

Benefits offered by this job

Wellbeing initiatives

Shared parental leave

Study assistance

Sabbaticals

Job summary

Elsevier is seeking an experienced ML Engineer/ML Ops expert to bridge Data Science and Engineering for GenAI-based features and secure, scalable services. You will participate in search/ranking quality, RAG, and knowledge-graph aware retrieval within Life Sciences applications.

You will collaborate with Data Scientists, Engineers and Product Managers to translate business problems into cutting-edge data science solutions, while ensuring content rights and confidentiality across enterprise

Qualifications

• 5+ years in ML engineering or shipping ML / GenAI systems to production.
• Experience with statistics, ML theory and NLP.
• Hands-on with AWS, Azure and/or Google cloud platforms.
• Knowledge of search/vector/graph tech (Elasticsearch/OpenSearch/Solr, Neo4j).
• Experience evaluating LLM models and data-centric ML workflows.

Responsibilities

• Automate and orchestrate ML workflows across cloud platforms.
• Maintain model registries and artifact stores for reproducibility.
• Develop and manage CI/CD for ML, with data validation and testing.
• Implement ML engineering using Sagemaker, MLflow, Azure ML.
• Design GAR+RAG systems; manage prompts and guardrails for LLMs.
• Design ML pipelines with vector DBs, ES/OpenSearch, graph DBs; evaluate IR metrics.

Skills

ML Engineering

MLOps

NLP

Cloud platforms

Model deployment

Tools

PyTorch

TensorFlow

PySpark

Elasticsearch/OpenSearch/Solr

Neo4j / Graph DBs

Job description

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
• 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.
• 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.