Senior MLOps Engineer: GenAI & Search Systems (Flexible)

Elsevier; City Of London; Vor Ort; GBP 100.000 - 140.000; Vollzeit

Zusammenfassung

Elsevier is seeking a senior data science and engineering professional to bridge ML/LLM engineering with scalable services for Scopus, ScienceDirect, and manuscript workflows. You will implement GenAI features, RAG and knowledge-graph–aware retrieval while upholding content rights and editorial confidentiality.

Lead ML pipelines, orchestration across AWS/Azure/Databricks, and production-grade SageMaker/MLflow workflows.

Qualifikationen

• Extensive experience ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.
• Strong Python, Java, or Scala engineering and software design skills.
• Hands-on experience with major cloud vendors (AWS/Azure/Google) and ML tooling.

Aufgaben

• ML & LLM Engineering, Search and Recommendation Engines.
• Automate and orchestrate ML workflows across cloud platforms and foundation model APIs.
• Maintain model registries, artifact stores, and governance for reproducibility.
• Develop and manage CI/CD for ML, including data validation and deployment.
• Implement ML pipelines using AWS SageMaker, MLflow, Azure ML; build SageMaker pipelines for recommendations.
• Design GAR+RAG components, prompt libraries, guardrails, and structured outputs for hosted LLMs.
• Develop pipelines with Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs; run offline IR metrics and A/B tests.
• Optimize infrastructure costs and stay current with GAI/NLP/RAG research.

Kenntnisse

Python/Java/Scala

ML & MLOps

NLP/IR

Cloud platforms

Tools

Elasticsearch/OpenSearch/Solr

Neo4j

PyTorch/TensorFlow

Spark

Jobbeschreibung

Elsevier is seeking a senior data science and engineering professional to bridge ML/LLM engineering with scalable services for Scopus, ScienceDirect, and manuscript workflows. You will implement GenAI features, RAG and knowledge-graph–aware retrieval while upholding content rights and editorial confidentiality.

Lead ML pipelines, orchestration across AWS/Azure/Databricks, and production-grade SageMaker/MLflow workflows.