This role supports Elsevier's large-scale research platforms by turning experimental NLP, search, and GenAI models into secure, reliable, and scalable production services. It focuses on ML and LLM engineering across cloud platforms, including building end-to-end ML pipelines, MLOps infrastructure, and CI/CD for models used in search, recommendations, and RAG-based systems. The position involves designing and operating retrieval, ranking, and evaluation pipelines, including IR metrics, LLM quality metrics, and A/B testing, while optimizing cost and performance at scale. You will collaborate closely with product managers, domain experts, data scientists, and operations engineers to deliver high-quality, responsible AI features over a massive scholarly corpus. The role suits an experienced ML engineer with strong cloud, search, and NLP expertise who wants to work at the intersection of GenAI, research content, and production-grade systems.
Key Responsibilities
ML & LLM Engineering, Search and Recommendation Engines
- Automate and orchestrate machine learning workflows across major cloud and AI platforms (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 popular MLOps platforms such as AWS SageMaker, MLflow, Azure ML
- End-to-end custom SageMaker pipelines for recommendation systems
- Design and implement the engineering components of GAR+RAG systems (e.g., query interpretation and reflection, chunking, embeddings, hybrid retrieval, semantic search), manage prompt libraries, guardrails and structured output for LLMs hosted on Bedrock/SageMaker or self-hosted
- Design and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBs
- Build evaluation pipelines: offline IR metrics (e.g., NDCG, MAP, MRR), LLM quality metrics (e.g., faithfulness, 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 the state‑of‑the‑art in our experiments and systems
Collaboration
- Partner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutions
- Collaborate and interface with Operations Engineers who deploy and run production infrastructure
Qualifications
- 5+ years in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production
- Strong Python, Java, and/or Scala engineering
- Experience with statistical analysis, machine learning theory and natural language processing
- Hands‑on experience with major cloud vendor solutions (AWS, Azure and/or Google)
- Search/vector/graph technologies (e.g., Elasticsearch / OpenSearch / Solr/ Neo4j)
- Experience in evaluating LLM models
- Background with scholarly publishing workflows, bibliometrics, or citation graphs
- A strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metrics
- Familiarity with ML frameworks, e.g., PyTorch, TensorFlow, PySpark
- Experience with large scale data processing systems, e.g., Spark
RELX is a global provider of information‑based analytics and decision tools for professional and business customers, enabling them to make better decisions, get better results and be more productive.
Our purpose is to benefit society by developing products that help researchers advance scientific knowledge; doctors and nurses improve the lives of patients; lawyers promote the rule of law and achieve justice and fair results for their clients; businesses and governments prevent fraud; consumers access financial services and get fair prices on insurance; and customers learn about markets and complete transactions.
Our purpose guides our actions beyond the products that we develop. It defines us as a company. Every day across RELX our employees are inspired to undertake initiatives that make unique contributions to society and the communities in which we operate.
Join our team and contribute to a culture of innovation, collaboration, and excellence. If you are ready to advance your career and make a significant impact, we encourage you to apply.
Work in a way that works for you
We promote a healthy work/life balance across the organization. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long‑term goals.
- Flexible working hours – flexing the times when you work in the day to help you fit everything in and work when you are the most productive.
Benefits
- Comprehensive Pension Plan
- Home, office, or commuting allowance.
- Generous vacation entitlement and option for sabbatical leave
- Maternity, Paternity, Adoption and Family Care leave
- Flexible working hours
- Personal Choice budget
- Internal communities and networks
- Various employee discounts
- Recruitment introduction reward
- Employee Assistance Program (global)
About the business
As a global leader in information and analytics, we help researchers and healthcare professionals advance science and improve health outcomes for the benefit of society. Building on our publishing heritage, we combine quality information and vast data sets with analytics to support visionary science and research, health education, and interactive learning, as well as exceptional healthcare and clinical practice. At Elsevier, your work contributes to the world's grand challenges and a more sustainable future. We harness innovative technologies to support science and healthcare to partner for a better world.
:quality(80))
:quality(80))
:quality(80))