ML Ops Engineer

Role & Responsibilities:

• Good knowledge of Azure and Databricks cloud platform and services.
  
  
• Strong hands-on experience in end-to-end ML solution orchestration for – 1) Data/Feature Engineering, 2) Model Training/Inferences, 3) Real-time/Batch Processing.
  
  
• Hands-on experience in backend development using FastAPI.
  
  
• Very good knowledge of MLOps concepts like data drift, model drift, online learning.
  
  
• Very good hands-on experience working with Python, PySpark, T-SQL, CI/CD on Github Actions.
  
  
• Collaborate with data engineers, data scientists and other stakeholders.
  
  
• At least 3 to 5 years of relevant experience in MLOps engineering role.
  
  
• Good to have knowledge in deployment of LLM based GenAI solutions.