Senior MLOps Engineer

Overview

In this role you translate research-driven NLP/IR/GenAI models into secure, scalable services that power Elsevier’s scholarly platforms. You will bridge data science and engineering to improve AI-based search, ranking, and knowledge-graph aware retrieval while upholding content rights and confidentiality. You’ll work across cloud and AI platforms to deploy and govern ML systems, with a focus on GAR+RAG, embeddings, and responsible AI. This is a hands-on, impact-focused position in a globally influential research information company. You will collaborate with cross-functional teams to drive scalable, production-ready AI solutions.

Pay / Benefits

• wellbeing initiatives
• shared parental leave
• study assistance
• sabbaticals
• flexible working hours

Responsibilities

• Automate and orchestrate ML workflows across AWS, Azure, Databricks, and foundation model APIs (e.g., OpenAI)
• Maintain and version model registries and artifact stores for reproducibility and governance
• Develop and manage CI/CD for ML, including data validation, model testing, and deployment
• Implement ML engineering solutions using MLOps platforms (AWS SageMaker, MLflow, Azure ML)
• End-to-end SageMaker pipelines for recommendation systems
• Design and implement GAR+RAG components (query interpretation, chunking, embeddings, hybrid retrieval, semantic search) with prompt libraries and guardrails
• Design and implement ML pipelines using Elasticsearch/OpenSearch/Solr, vector DBs, graph DBs
• Build evaluation pipelines for offline IR metrics, LLM quality metrics, and A/B testing
• Optimize infrastructure costs through monitoring, scaling, and efficient resource use
• Stay current with GAI, NLP, and RAG research and apply state-of-the-art to experiments and systems
• Collaborate with SMEs, Product Managers, Data Scientists, and Responsible AI experts to translate problems into data science solutions
• Collaborate with Operations Engineers to deploy and run production infrastructure

Key requirements

• Extensive experience in ML Engineering, MLOps, and 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 with AWS, Azure and/or Google Cloud
• Experience with search/vector/graph technologies (Elasticsearch/OpenSearch/Solr, Neo4j)
• Experience evaluating LLM models
• Background with scholarly publishing workflows, bibliometrics, or citation graphs
• Understanding of DS Life Cycle including feature engineering, model training, and evaluation metrics
• Familiarity with ML frameworks (PyTorch, TensorFlow, PySpark)
• Experience with large-scale data processing (Spark)

• collaboration
• problem-solving
• communication
• MLOps
• Python
• Java

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