Are you passionate about building scalable AI and machine learning systems that power world-leading research and healthcare platforms? Do you enjoy turning cutting-edge NLP, search, recommendation, and Generative AI innovations into reliable, secure, and production-ready solutions that create real-world impact?

About the Team

Our integral team support products education electronic health records that introduce students to digital charting and prepare them to document care in today’s modern clinical environment. We have a very stable product that we’ve worked to get to and strive to maintain. Our team values trust, respect, collaboration, agility, and quality.

About the Role

Join the team that powers Elsevier’s research platforms—Scopus/Scopus AI, ScienceDirect/ScienceDirect AI, and journal submission & peer review workflows. You will bridge Data Science and Engineering to turn experimental NLP/IR/GenAI models into secure, reliable, and scalable services. Our systems operate over one of the world’s largest scholarly corpora, so you’ll work on AI-based features (GenAI,



Agentic AI, RAG, etc.) search/ranking quality, and knowledge graph aware retrieval while enforcing content rights and editorial confidentiality.

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 sea

📌 Senior MLOps Engineer (Ciudad de México)
🏢 Lexisnexis Risk Solutions
📍 Ciudad de México