About the Company
Elsevier is a global leader in information and analytics, supporting researchers, clinicians, and life sciences professionals to advance scientific discovery and improve health outcomes. The organization combines trusted content, vast data sets, and advanced analytics to deliver innovative research and healthcare solutions. Elsevier develops intelligent discovery platforms, leveraging technologies such as retrieval-augmented generation (RAG), semantic search, and generative AI to make knowledge more discoverable, connected, and actionable across disciplines.
About the Role
The ML/LLM Engineering role focuses on transforming experimental NLP, search, and generative AI models into secure, reliable, and scalable production services. Responsibilities include building end-to-end ML pipelines, MLOps infrastructure, and CI/CD for models powering search, recommendations, and RAG-based systems.
Key responsibilities
- Automating and orchestrating machine learning workflows across cloud and AI platforms (AWS, Azure, Databricks, foundation model APIs).
- Maintaining model registries and artifact stores to ensure reproducibility and governance.
- Developing and managing CI/CD for ML, including automated data validation, model testing, and deployment.
- Implementing ML engineering solutions using MLOps platforms such as AWS SageMaker, MLflow, and Azure ML.
- Designing and operating pipelines for recommendation systems and GAR+RAG systems, including query processing, embeddings, hybrid retrieval, semantic search, prompt libraries, and structured LLM outputs.
- Developing ML pipelines leveraging Elasticsearch/OpenSearch/Solr, vector databases, and graph databases.
- Building evaluation pipelines for offline IR metrics (NDCG, MAP, MRR), LLM quality metrics, and A/B testing.
- Optimizing infrastructure costs through monitoring, scaling strategies, and efficient resource utilization.
- Staying current with generative AI, NLP, and RAG research and applying state-of-the-art techniques in production systems.
- Collaborating with subject-matter experts, product managers, data scientists, Responsible AI experts, and operations engineers to translate business challenges into data science solutions.
Requirements
- 5+ years of experience in ML engineering, MLOps, and production-scale ML or search/GenAI systems.
- Strong Python, Java, and/or Scala programming skills.
- Expertise in statistical analysis, machine learning theory, and natural language processing.
- Hands-on experience with major cloud platforms (AWS, Azure, GCP).
- Knowledge of search, vector, and graph technologies (e.g., Elasticsearch, OpenSearch, Solr, Neo4j).
- Experience in evaluating LLM models.
- Understanding of scholarly publishing workflows, bibliometrics, or citation graphs.
- Familiarity with the data science lifecycle, including feature engineering, model training, and evaluation metrics.
- Experience with ML frameworks (PyTorch, TensorFlow, PySpark) and large-scale data processing systems (e.g., Spark).
Benefits
- Flexible working hours to support productivity and work-life balance.
- Comprehensive pension plan.
- Home, office, or commuting allowance.
- Generous vacation entitlement with option for sabbatical leave.
- Maternity, paternity, adoption, and family care leave.
- Personal Choice budget.
- Access to internal communities and networks.
- Various employee discounts and recruitment introduction rewards.
- Global Employee Assistance Program.
- Opportunities to contribute to cutting-edge AI research and production-scale systems.