Senior MLOps Engineer — GenAI & NLP Cloud Pipelines

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

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.