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Elsevier

Global STM publisher of journals.

Senior MLOps Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeMexico City, CDMX, Mexico$115.4k - $192.3k

About the Job

Requirements

  • At least 3 years of experience in machine-learning engineering, MLOps platforms, or shipping machine-learning, search, or generative-AI systems to production.
  • Strong engineering skills in Python, Java, and/or Scala.
  • Experience with statistical analysis, machine-learning theory, and natural language processing.
  • Hands-on experience with major cloud vendor solutions such as AWS, Azure, and/or Google Cloud.
  • Experience with search, vector, or graph technologies such as Elasticsearch, OpenSearch, Solr, or Neo4j.
  • Experience evaluating large language models.
  • Background with scholarly publishing workflows, bibliometrics, or citation graphs.
  • Strong understanding of the data science life cycle, including feature engineering, model training, and evaluation metrics.
  • Familiarity with machine-learning frameworks such as PyTorch, TensorFlow, or PySpark.
  • Experience with large-scale data processing systems such as Spark.

Responsibilities

  • Automate and orchestrate machine-learning workflows across cloud and AI platforms, including AWS, Azure, Databricks, and foundation-model APIs such as OpenAI.
  • Maintain and version model registries and artifact stores to support reproducibility and governance.
  • Develop and manage CI/CD for machine learning, including automated data validation, model testing, and deployment.
  • Implement machine-learning engineering solutions using MLOps platforms such as AWS SageMaker, MLflow, and Azure ML.
  • Build end-to-end custom SageMaker pipelines for recommendation systems.
  • Design and implement engineering components of GAR+RAG systems, including query interpretation and reflection, chunking, embeddings, hybrid retrieval, and semantic search; manage prompt libraries, guardrails, and structured output for LLMs hosted on Bedrock/SageMaker or self-hosted.
  • Design and implement machine-learning pipelines using Elasticsearch, OpenSearch, Solr, vector databases, and graph databases.
  • Build evaluation pipelines using offline information-retrieval metrics such as NDCG, MAP, and MRR; LLM quality metrics such as faithfulness and grounding; and A/B testing.
  • Optimize infrastructure costs through monitoring, scaling strategies, and efficient resource utilization.
  • Stay current with generative-AI research, natural language processing, and retrieval-augmented generation, and apply state-of-the-art approaches in experiments and systems.
  • Partner with subject-matter experts, product managers, data scientists, and Responsible AI experts to translate business problems into data-science solutions.
  • Collaborate and interface with operations engineers who deploy and run production infrastructure.

About the Company

Elsevier

Global STM publisher of journals.

Elsevier provides scientific, medical, and technical content and analytics to researchers, educators, clinicians, and institutions worldwide through journals, books, databases, and digital platforms. It operates by offering access to peer-reviewed articles and professional literature via online platforms, along with tools for discovery, citation tracking, research management, and clinical decision support. Unlike other publishers, Elsevier combines a broad portfolio of STM content with integrated analytics and workflow tools, serving researchers, healthcare professionals, and academic institutions on a global scale. Its goal is to enable the advancement of knowledge and improve health outcomes by delivering reliable, accessible information and data-driven solutions across the research lifecycle.

Company size
10,001+
Company stage
Not listed
Total funding
Not listed
Headquarters
Amsterdam, Netherlands
Founded
1880

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