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(CoreFitQs) - Machine Learning Ops

• How many years of experience do you have working in ML Ops or a similar machine learning engineering role?✱

  • Less than 2 years
  • 2 to 3 years
  • More than 3 years
• Do you have experience designing and deploying end-to-end machine learning solutions in production?✱

  • No
  • Some, in collaboration with other teams
  • Yes, I have led or owned end-to-end ML pipelines
• How would you rate your Python programming skills?✱

  • Basic
  • Intermediate
  • Advanced
• How experienced are you with SQL and Spark for working with large-scale data?✱

  • Limited or no experience
  • Comfortable using both in data pipelines
  • Advanced usage in production environments
• Which of the following best describes your experience with cloud platforms (AWS, GCP)?✱

  • No experience
  • Limited use in small projects
  • Hands-on experience managing cloud-based ML workloads
• What is your experience level with Docker and Git?✱

  • I have used one or both minimally
  • I use them regularly for development
  • I use them for production-grade solutions and automation
• Have you worked with any ML Ops orchestration tools such as Airflow, SageMaker Studio, or CloudFormation?✱

  • No experience
  • Familiar with some tools or have used in limited contexts
  • Regular use of one or more tools in ML workflows
• What is your English proficiency level (written and spoken)?*✱

  • Basic (can read and understand documentation)
  • Intermediate (can communicate in team meetings)
  • Advanced (can lead discussions and write clearly)
• What is your expected yearly compensation (in USD) for a full-time remote role like this? Please indicate a range (e.g., "$66,000–$70,000"). If you're open to flexibility depending on the scope, feel free to mention that.✱
• What is your current notice period?✱

  • 1 to 2 weeks
  • 2 to 3 weeks
  • More than 4 weeks

Demographic Survey