- - - - - - - - - - - -
KEY EXPECTED ACHIEVEMENTS
Business understanding & Solution implementation
• The business need is understood and formalized in a descriptive datasheet or specifications
• The methods are identified through external researches and selected by their theoretical bases, advantages and drawbacks.
Roles & Responsibilities:
- Responsible for design and implementation of Application MLOPs frameworks.
- Responsible for Deploying the ML Model end to end
- Expert on Azure DevOps Pipelines
- Responsible for Model and Data Drift frameworks.
- Build and maintain CI/CD/CT pipeline across azure cloud platform for Data science projects.
- Experience in MLFlow.
- Envision, implement and rollout best MLOPs/DevOps tools and automation.
- Nice to have LLMOPS knowledge
