Senior MLOps Engineer

RELX; Ciudad de México; Híbrido; MXN 1.984.000 - 3.306.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Private Medical Plan

Savings Fund with company matching

Life insurance

Grocery voucher

Vacation Bonus

Learning & development resources

Employee Assistance Programme

Awards for service milestones

Charity time off

Descripción de la vacante

Elsevier is seeking an experienced ML Engineer to join the team powering research platforms, including Scopus AI and ScienceDirect AI. You will bridge Data Science and Engineering to turn NLP/IR/GenAI models into secure, scalable services within a scholarly data corpus.

The role covers ML engineering, CI/CD for ML, GAR/RAG systems, and evaluation of LLMs. Hybrid work in Mexico City (Reforma) with emphasis on collaboration and responsible AI.

Formación

• 3–5+ years in ML Engineering or MLOps, shipping ML or GenAI systems to production.
• Strong Python, Java, and/or Scala engineering.
• Experience with NLP, ML concepts and evaluation metrics.
• Experience with major cloud vendor solutions (AWS/Azure/Google).
• Proficiency with search/vector/graph technologies (Elasticsearch/OpenSearch/Solr/Neo4j).
• Experience evaluating LLM models and scholarly publishing workflows is a plus.
• Familiarity with ML frameworks (PyTorch, TensorFlow, PySpark) and Spark.

Responsabilidades

• Automate and orchestrate ML workflows across cloud platforms.
• Maintain model registries and artifact stores for reproducibility.
• Develop and manage CI/CD pipelines for ML and data validation.
• Implement ML solutions using SageMaker, MLflow, Azure ML.
• Design GAR+RAG components and prompt libraries for LLMs on Bedrock/SageMaker.
• Build and optimize pipelines with Elasticsearch/OpenSearch/Solr, vector/graph DBs.
• Create evaluation pipelines with IR metrics and LLM quality checks.
• Collaborate with PMs, Data Scientists, and Responsible AI experts.

Conocimientos

Python

Java

Scala

NLP

LLM

Cloud (AWS/Azure/Google)

Elasticsearch/OpenSearch/Solr

Neo4j

PyTorch

TensorFlow

PySpark

Spark

Herramientas

SageMaker

MLflow

GCP/AWS/AzureML

Databricks

Descripción del empleo

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 Team

Our 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 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 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.

Required Qualifications

• 3–5+ 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

Work in a Way That Works for You

We 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 Pattern

Working 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.

Benefits

• Private Medical, Dental and Vision Plan, coverage for employee and eligible dependents
• Savings Fund with company matching contributions
• Comprehensive life insurance policy
• Grocery voucher
• Vacation Bonus, salary supplement based on vacation days taken
• Minor medical expenses discount card for minor medical expenses and outpatient services
• Access to learning and development resources
• Support for personal and work-related challenges through an Employee Assistance Programme
• Awards to recognise key service milestones
• Time off to support the charities and causes that matter to you

About The Business

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.

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.