AI Platform Engineer

Summary: Develop and scale enterprise AI platforms.

Responsibilities:

  • Build self-service platforms for model deployment.
  • Integrate MLOps tools (e.g., MLflow, Kubeflow).
  • Enable multi-tenancy and resource management.
    Skills:
  • Proficiency in Go/Python; knowledge of Ray or Seldon Core.

Key Process: Building Scalable AI Platforms

  • Inputs: Platform requirements, MLOps tools, user feedback.
  • Activities:
    • Develop APIs for model deployment.
    • Enable multi-tenant resource allocation.
    • Integrate monitoring/alerting systems.
  • Outputs: Self-service AI platforms, developer documentation.
  • Stakeholders: Data engineers, DevOps, end-users.
  • Tools: Kubeflow, Seldon Core, Docker.

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