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 Our TeamOur global 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 RoleJoin 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 ResponsibilitiesML & LLM Engineering, Search and Recommendation EnginesAutomate 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 governanceDevelop 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-hostedDesign and implement ML pipelines that utilize Elasticsearch/OpenSearch/Solr, vector DBs, and graph DBsBuild 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 utilizationStay current with the latest GAI research, NLP and RAG and apply the state-of-the-art in our experiments and systemsCollaborationPartner with Subject-Matter Experts, Product Managers, Data Scientists and Responsible AI experts to translate business problems into cutting edge data science solutionsCollaborate and interface with Operations Engineers who deploy and run production infrastructure.Required Qualifications3–5+ years in ML Engineering, MLOps platforms, shipping ML or search/GenAI systems to production.Strong Python, Java, and/or Scala engineeringExperience with statistical analysis, machine learning theory and natural language processingHands‐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 modelsBackground with scholarly publishing workflows, bibliometrics, or citation graphsA strong understanding of the Data Science Life Cycle including feature engineering, model training, and evaluation metricsFamiliarity with ML frameworks, e.g., PyTorch, TensorFlow, PySparkExperience with large scale data processing systems, e.g., SparkWork in a Way That Works for YouWe promote a healthy work/life balance across the organisation.
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.This is a hybrid role in Mexico City (Reforma) Our teams operate in a flexible hybrid work model, combining in-person collaboration with remote flexibility.
You'll be expected to participate in regular team meetings and engineering rituals in line with your team's cadence.Working PatternWorking flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.BenefitsPrivate Medical, Dental and Vision Plan, coverage for employee and eligible dependentsSavings Fund with company matching contributionsComprehensive life insurance policyGrocery voucherVacation Bonus, salary supplement based on vacation days takenMinor medical expenses discount card for minor medical expenses and outpatient servicesAccess to learning and development resourcesSupport for personal and work-related challenges through an Employee Assistance ProgrammeAwards to recognise key service milestonesTime off to support the charities and causes that matter to youAbout The BusinessA integral 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.U.S. National Base Pay Range: $115,400 - $192,300.
Geographic differentials may apply in some locations to better reflect local market rates.We know your well-being and happiness are key to a long and successful career.
We are delighted to offer country specific benefits.
#J-*****-Ljbffr

📌 Senior Mlops Engineer (Xico)
🏢 RELX
📍 Xico