Senior MLOps Engineer: Scalable AI for Research & Health

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

Benefits offered by this job

Wellbeing initiatives

Shared parental leave

Study assistance

Sabbaticals

Flexible working hours

Job summary

Elsevier seeks a Senior MLOps Engineer to build scalable AI/ML systems powering research and healthcare platforms. You will bridge Data Science and Engineering to deploy secure, reliable AI services across cloud platforms, influencing search, recommendations, and knowledge graphs.

You will work on GenAI, RAG, and knowledge retrieval features while ensuring content rights and editorial confidentiality within a large scholarly corpus.

Qualifications

• 3–5+ years in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.
• Strong Python, Java, and/or Scala engineering.
• Experience with cloud vendor solutions (AWS, Azure and/or Google).
• Search/vector/graph technologies (Elasticsearch / OpenSearch / Solr / Neo4j).
• Experience in evaluating LLM models.
• Background with scholarly publishing workflows, bibliometrics, or citation graphs.
• 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.

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-to-end custom SageMaker pipelines for recommendation systems.
• Design and implement the engineering components of GAR+RAG systems, manage prompt libraries, guardrails and structured output for LLMs.
• Design and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs.
• Build evaluation pipelines: offline IR metrics, LLM quality metrics, 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.

Skills

ML Engineering

MLOps

Python

Java

Scala

Tools

Elasticsearch

OpenSearch

Solr

Neo4j

Job description

Elsevier seeks a Senior MLOps Engineer to build scalable AI/ML systems powering research and healthcare platforms. You will bridge Data Science and Engineering to deploy secure, reliable AI services across cloud platforms, influencing search, recommendations, and knowledge graphs.

You will work on GenAI, RAG, and knowledge retrieval features while ensuring content rights and editorial confidentiality within a large scholarly corpus.