Senior MLOPs

Elsevier; Amsterdam; On-site; EUR 54,000 - 90,000; Full time

Job summary

Elsevier is seeking a Senior MLops engineer to bridge Data Science and Engineering for secure, scalable ML services in Life Sciences. You will work on GenAI, agentic AI, RAG, and knowledge graph retrieval across NLP/IR pipelines.

The role involves building end-to-end ML workflows, maintaining model registries, and optimizing deployments while collaborating with cross-functional teams in a data-driven environment.

Qualifications

• 5+ years in ML engineering, MLOps platforms, or shipping ML to production.
• Strong Python, Java, and/or Scala engineering proficiency.
• Experience with statistics, ML theory, and NLP.
• Hands-on with AWS, Azure and/or Google cloud solutions.
• Experience with search/vector/graph tech (Elasticsearch/OpenSearch/Solr, Neo4j).
• Experience in evaluating LLM models.
• Background with scholarly publishing workflows, bibliometrics, or citation graphs.
• Understanding of the DS life cycle: feature engineering, model training, evaluation.
• Familiarity with ML frameworks like PyTorch, TensorFlow, PySpark.
• Experience with large-scale data processing (Spark).

Responsibilities

• Automate and orchestrate ML workflows across cloud platforms (AWS, Azure, Databricks, and OpenAI APIs).
• Maintain and version model registries and artifact stores for reproducibility.
• Develop and manage CI/CD for ML, including data validation, model testing, and deployment.
• Implement ML engineering solutions using SageMaker, MLflow, Azure ML.
• End-to-end SageMaker pipelines for recommendation systems.
• Design GAR+RAG components: query interpretation, embeddings, hybrid retrieval, structured output; manage prompts and guardrails.
• Design ML pipelines using Elasticsearch/OpenSearch/Solr, vector DBs, graph DBs; set up offline IR metrics and LLM quality metrics; conduct A/B testing.
• Optimize infrastructure costs via monitoring, scaling strategies, and resource efficiency.
• Stay current with GAI, NLP, and RAG research and apply it in experiments and systems.
• Collaborate with Data Scientists, Engineers, PMs, and Responsible AI experts; interface with Operations Engineers.

Skills

ML Engineering

Python

Java

Scala

NLP

Cloud (AWS)

Azure

Google Cloud

Elasticsearch/OpenSearch/Solr

Neo4j

LLM evaluation

Data Science Life Cycle

PyTorch/TensorFlow

Spark

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

• 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.

We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120.

We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law. USA Job Seekers: EEO Know Your Rights.

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