Senior MLOPs

In this role you bridge Data Science and Engineering to turn NLP/IR/GenAI experiments into secure, scalable services for Elsevier’s life sciences products. You will collaborate with cross-functional teams to enable AI-based features, optimize retrieval and knowledge-graph driven search, and uphold content rights and confidentiality. You’ll own ML engineering, MLOps, and end-to-end pipelines across major cloud platforms, while applying state-of-the-art research to real-world life sciences challenges. This is an impact-focused role in a mission-driven team advancing science and healthcare.

• wellbeing initiatives
• shared parental leave
• study assistance
• sabbaticals
• healthy work/life balance
• country-specific benefits

• Automate and orchestrate ML workflows across AWS, Azure, Databricks, and foundation model APIs (OpenAI)
• Maintain 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 using MLOps platforms (SageMaker, MLflow, Azure ML)
• End-to-end SageMaker pipelines for recommendation systems
• Design GAR+RAG components (query interpretation, embeddings, retrieval, prompts, guardrails) for LLMs
• Design ML pipelines with Elasticsearch/OpenSearch/Solr, vector databases, and graph databases
• Build evaluation pipelines (offline IR metrics, LLM quality metrics) and perform A/B testing
• Optimize infrastructure costs and improve scalability
• Stay current with GenAI, NLP, and RAG research and apply advancements
• Collaborate with Data Scientists, Engineers, PMs, and Responsible AI experts; interface with Operations Engineers

• 5+ years in ML Engineering, MLOps, or production ML/GenAI systems
• Strong Python, Java, and/or Scala
• Experience with statistical analysis, ML theory, and NLP
• Hands-on with AWS, Azure, and/or Google Cloud
• Experience with search/vector/graph tech (Elasticsearch/OpenSearch/Solr, Neo4j)
• Experience evaluating LLMs
• Background in scholarly publishing workflows, bibliometrics, or citation graphs
• Understanding of DS life cycle: feature engineering, model training, evaluation
• Familiarity with ML frameworks (PyTorch, TensorFlow, PySpark); Spark experience
• Experience with large-scale data processing systems (Spark)

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