Senior Ml Ops Engineer

Elsevier

Base: $95,300 - $158,800 (us national); geographic...
Hybrid
Machine learning ops engineering experience
Aws sagemaker and mlflow implementation
Genai and rag system architecture
This role bridges Data Science and Engineering to turn experimental NLP and GenAI models into secure, reliable, and scalable services for Elsevier's Health platforms

Job Summary

  • This role bridges Data Science and Engineering to turn experimental NLP and GenAI models into secure, reliable, and scalable services for Elsevier's Health platforms.
  • The team operates over one of the world's largest medical and scholarly landscapes, focusing on AI-driven automated clinical and content workflows.
  • Candidates will design evaluation pipelines including offline IR metrics and LLM quality assessments while optimizing infrastructure costs.

Matching Summary

This role bridges Data Science and Engineering to turn experimental NLP and GenAI models into secure, reliable, and scalable services for Elsevier's Health platforms.

Salary

Base: $95,300 - $158,800 (US National); Geographic differentials apply; Bonus: Eligible for annual incentive bonus

Skills & Requirements

Must-have

  • Machine Learning Ops Engineering experience
  • AWS SageMaker and MLflow implementation
  • GenAI and RAG system architecture
  • Python programming proficiency
  • Elasticsearch or vector database management
  • CI/CD for machine learning workflows
  • Cloud platform orchestration (AWS/Azure)

Nice-to-have

  • Background in health technology workflows
  • Java or Scala programming skills
  • Experience with knowledge graph retrieval
  • Statistical analysis and ML theory knowledge
  • Collaborative team environment preference

Key Requirements

  • Current experience shipping ML or search systems to production
  • Strong understanding of the Data Science Life Cycle
  • Hands-on experience with major cloud vendor solutions

Work Rights

Not specified

Tailored Resume

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