Jobs at Elsevier

Design, build, evaluate, and scale production-ready AI solutions (LLMs, NLP, retrieval, and generative AI) to support scientific discovery. Develop RAG systems, semantic search, summarization, QA, ranking, recommendation, and evaluation frameworks. Integrate knowledge graphs and domain metadata, partner with engineering and product teams, lead technical decisions, and mentor data scientists.

Leads global commercial strategy workstreams for a healthcare education portfolio, evaluating market opportunities, investments, pricing, monetization, partnerships, and regional prioritization. Conducts data-driven research and builds revenue models to guide portfolio and product decisions. Collaborates with Product, Strategy, Marketing, Sales, and Customer Success teams across global markets, translating customer, competitive, and market insights into recommendations that support sustainable growth.

Build and ship production-grade AI features, including RAG pipelines, tool-augmented agents, and agentic workflows. Integrate AI capabilities into enterprise systems, contribute to evaluation, observability, guardrails, and outcome measurement, and maintain reusable technical patterns. Collaborate with stakeholders to ensure solutions fit real workflows, identify risks, complete bug fixes, document implementations, and support handover and capability-building.

Build and maintain reliable, scalable platforms and services. Responsibilities include monitoring, incident response, post-mortems, disaster recovery testing, production automation, AI service deployment, infrastructure documentation, and reliability testing. The role requires advanced Terraform, AWS operations, GitHub Actions CI/CD, containerized ECS Fargate environments, networking and security expertise, Linux and scripting skills, observability, and developer enablement across engineering teams.

As a Senior ML Ops Engineer, you will bridge Data Science and Engineering to automate ML workflows, manage CI/CD for ML systems, and collaborate with various teams to implement solutions for AI features and services.

Design, develop, and deploy ML, NLP, and generative-AI solutions (including LLMs and RAG) to improve search, retrieval, recommendation, and question-answering across scientific publications and datasets. Build scalable pipelines, evaluation frameworks, and production-quality Python code while collaborating with engineering, product, UX, and domain experts to measure and improve model reliability, relevance, and user impact.

The Account Manager will drive sales and renewals, manage customer relationships, and support customers with diverse needs while achieving bookings and conducting market evaluations.

Lead and develop high-performing data science teams to build AI-powered search, retrieval, NLP, generative AI, and knowledge systems for scientific discovery. Define strategy, drive production deployments, establish evaluation and responsible AI practices, partner with product and engineering, mentor staff, and influence senior stakeholders to deliver measurable impact.