Senior Mlops Engineer

Case Law Reporter

Multiple Locations
Base: not specified; bonus/equity: not specified; ...
Hybrid
Mlops platforms
Ml and llm engineering
Cloud platforms
This role supports Elsevier’s large-scale research platforms by turning experimental NLP, search, and GenAI models into secure, reliable, and scalable production services

Job Summary

  • This role supports Elsevier’s large-scale research platforms by turning experimental NLP, search, and GenAI models into secure, reliable, and scalable production services.
  • The position involves designing and operating retrieval, ranking, and evaluation pipelines, including IR metrics, LLM quality metrics, and A/B testing, while optimizing cost and performance at scale.
  • We promote a healthy work/life balance across the organization.

Matching Summary

This role supports Elsevier’s large-scale research platforms by turning experimental NLP, search, and GenAI models into secure, reliable, and scalable production services.

Salary

Base: Not specified; Bonus/Equity: Not specified; Benefits: Not specified

Skills & Requirements

Must-have

  • MLOps platforms
  • ML and LLM engineering
  • cloud platforms
  • end-to-end ML pipelines
  • CI/CD for models
  • retrieval, ranking, evaluation pipelines
  • scholarly corpus

Nice-to-have

  • collaboration with product managers
  • collaboration with domain experts
  • collaboration with data scientists
  • collaboration with operations engineers
  • responsible AI features
  • state-of-the-art GAI research

Key Requirements

  • 5+ years in ML Engineering
  • MLOps platforms experience
  • shipping ML or search/GenAI systems to production
  • Strong Python, Java, and/or Scala engineering
  • Experience with statistical analysis
  • machine learning theory
  • natural language processing
  • major cloud vendor solutions (AWS, Azure and/or Google)
  • Search/vector/graph technologies
  • Experience in evaluating LLM models
  • Background with scholarly publishing workflows
  • bibliometrics
  • citation graphs
  • Data Science Life Cycle understanding
  • ML frameworks (PyTorch, TensorFlow, PySpark)
  • large scale data processing systems (Spark)

Work Rights

Not specified

Tailored Resume

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