Senior MLOps

In this role you will bridge data science and engineering to turn experimental NLP/IR/GenAI models into secure, scalable services powering Elsevier’s scholarly platforms. You will work on AI-based features, search and knowledge retrieval, and ensure governance and reliability across large corpora. You’ll collaborate with cross-functional teams to translate problems into production-ready solutions while optimizing costs and staying current with state-of-the-art AI research. This is an opportunity to shape how researchers access trusted knowledge at scale.

• flexible working hours
• wellbeing initiatives
• shared parental leave
• study assistance
• sabbaticals
• country-specific benefits

• Automate and orchestrate ML workflows across cloud and AI platforms (AWS, Azure, Databricks, OpenAI)
• Maintain model registries and artifact stores for reproducibility and governance
• Develop and manage CI/CD for ML including data validation, testing, and deployment
• Implement ML engineering with MLOps platforms (AWS SageMaker, MLflow, Azure ML)
• Build SageMaker-based end-to-end pipelines for recommendation systems
• Design GAR+RAG components (query interpretation, embeddings, hybrid retrieval, semantic search) with prompt libraries and guardrails
• Design ML pipelines using Elasticsearch/OpenSearch/Solr, vector/graph DBs
• Create evaluation pipelines for IR metrics, LLM quality, and A/B testing
• Optimize infrastructure costs via monitoring and scalable resource use
• Stay current with GAI, NLP, and RAG research and apply in production

• 3–5+ years in ML engineering, MLOps, or production ML/GenAI systems
• Strong Python, Java, and/or Scala
• Experience with statistics, ML theory, and NLP
• Hands-on cloud experience (AWS, Azure, or Google)
• 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 DS life cycle (feature engineering, training, evaluation)
• Familiarity with ML frameworks (PyTorch, TensorFlow, PySpark)
• Experience with large-scale data processing (Spark)

• Collaborative mindset with cross-functional teams
• Strong communication and problem-solving
• Adaptability and eagerness to stay current with research trends
• Python, Java, or Scala
• AWS, Azure, or Google Cloud
• Elasticsearch/OpenSearch/Solr, Neo4j