RRemoteville

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MLOps Engineer

Skills
Data ScienceDialectical Behavior TherapyDriftEnglishGood Clinical PracticeGoogle Cloud PlatformService-Level Agreements
Role

What the job involves

The main requirements, responsibilities and hiring steps.

Requirements

  • Track record of putting models into production with Data Scientists
  • Deep understanding of the ML lifecycle from prototype to production
  • Experience with real-time serving feature stores autoscaling and blue green deployments
  • Strong Python and SQL skills
  • Hands-on GCP experience especially Vertex AI Kubeflow pipelines and BigQuery or equivalent cloud fluency
  • Solid CI/CD and production monitoring experience including model registries versioning drift detection and automated retraining
  • Conversational-level English language skills

Nice to have

  • LLM evaluation experience
  • Observability tooling exposure
  • RAG systems in production
  • MLOps standards experience
  • GenAI cost management experience

Day to day

  • Own the productionisation of machine learning work end to end, from prototype to production
  • Build and maintain real-time serving, CI/CD, monitoring, and deployment infrastructure for ML and GenAI systems
  • Collaborate closely with Data Scientists, Platform, and Engineering to deliver reliable systems that perform at scale

Hiring process

  • Submit your application and CV in English
  • Applications are reviewed by AI and then by recruiters and hiring managers
  • Final decisions are made by people
  • You may ask how AI was used in your application