Senior MLOps Engineer — Flexible Hours & GenAI

Elsevier; City Of London; On-site; GBP 85,000 - 125,000; Full time

Job summary

Elsevier is seeking an ML Engineer to bridge Data Science and Engineering for scalable NLP/IR/GenAI services across its research platforms. You will work on AI-based features, search quality, and knowledge-graph aware retrieval within a secure, editorially controlled environment.

You will collaborate with cross-functional teams to deliver end-to-end ML pipelines, governance, and cost-efficient infrastructure while staying at the forefront of AI and machine learning research.

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 statistical analysis, ML theory and natural language processing
• Hands-on experience with major 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 ML workflows across AWS, Azure, Databricks, and LLM APIs.
• 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 MLOps platforms such as AWS SageMaker, MLflow, Azure ML.
• End-to-end custom SageMaker pipelines for recommendation systems.
• Design and implement GAR+RAG components (query interpretation, embeddings, hybrid retrieval, semantic search); manage prompts.
• Design and implement ML pipelines using Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs
• Build evaluation pipelines: offline IR metrics (NDCG, MAP, MRR), LLM quality metrics, A/B testing.
• Optimize infrastructure costs via monitoring, scaling and resource utilization
• Stay current with GAI research, NLP and RAG; apply state-of-the-art in experiments

Skills

Python

Java/Scala

NLP

AWS/Azure/Google Cloud

Elasticsearch/OpenSearch/Solr/Neo4j

LLM evaluation

PyTorch/TensorFlow

Spark

Tools

SageMaker

MLflow

Azure ML

Job description

Elsevier is seeking an ML Engineer to bridge Data Science and Engineering for scalable NLP/IR/GenAI services across its research platforms. You will work on AI-based features, search quality, and knowledge-graph aware retrieval within a secure, editorially controlled environment.

You will collaborate with cross-functional teams to deliver end-to-end ML pipelines, governance, and cost-efficient infrastructure while staying at the forefront of AI and machine learning research.