Senior MLOPs

Join the Data Science Life Sciences team at Elsevier, a diverse group focused on GenAI, ML, and NLP. The team develops best-in-class enrichment pipelines for Elsevier’s life science products such as Reaxys, Embase, and Pharmapendium.

About the role

In this role, you will bridge Data Science and Engineering to turn experimental NLP, IR, and GenAI models into secure, reliable, and scalable services. The work supports R&D in the Chemistry and Biology domain through AI-based features including GenAI, Agentic AI, RAG, search and ranking quality, and knowledge graph-aware retrieval, while enforcing content rights and confidentiality.

Key responsibilities

ML & LLM Engineering, Search and Recommendation Engines

• Automate and orchestrate machine learning workflows across major cloud and AI platforms including AWS, Azure, Databricks, and foundation model APIs such as OpenAI.
• 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, and Azure ML.
• Build end-to-end custom SageMaker pipelines for recommendation systems.
• Design and implement the engineering components of GAR+RAG systems, including query interpretation and reflection, chunking, embeddings, hybrid retrieval, semantic search, prompt libraries, guardrails, and structured output for LLMs hosted on Bedrock, SageMaker, or self-hosted environments.
• Design and implement ML pipelines that utilize Elasticsearch, OpenSearch, Solr, vector databases, and graph databases.
• Build evaluation pipelines using offline IR metrics such as NDCG, MAP, and MRR, as well as LLM quality metrics including faithfulness and grounding, and A/B testing.
• Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization.
• Stay current with the latest GAI research, NLP, and RAG, and apply state-of-the-art approaches in experiments and systems.

Collaboration

• Partner with Data Scientists, Engineers, Subject Matter Experts, Product Managers, and Responsible AI experts to translate business problems into cutting-edge data science solutions.
• Collaborate with Operations Engineers who deploy and run production infrastructure.

Required qualifications

• 5+ years in ML Engineering, MLOps platforms, and shipping ML or search/GenAI systems to production.
• Strong engineering skills in Python, Java, and/or Scala.
• Experience with statistical analysis, machine learning theory, and natural language processing.
• Hands-on experience with major cloud vendor solutions such as AWS, Azure, and/or Google Cloud.
• Experience with search, vector, and graph technologies such as Elasticsearch, OpenSearch, Solr, and Neo4j.
• Experience in evaluating LLM models.
• Background with scholarly publishing workflows, bibliometrics, or citation graphs.
• Strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metrics.
• Familiarity with ML frameworks such as PyTorch, TensorFlow, and PySpark.
• Experience with large-scale data processing systems such as Spark.

Work in a way that works for you

Elsevier promotes a healthy work/life balance across the organization and offers an appealing working prospect for its people. With numerous wellbeing initiatives, shared parental leave, study assistance, and sabbaticals, the company supports both immediate responsibilities and long-term goals.

About the business

Elsevier is a global leader in information and analytics, helping researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. The company combines quality information, extensive data sets, and analytics to support science and research, health education and interactive learning, as well as healthcare and clinical practice.