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Machine Learning Operations Engineer (MLOps)

Job description

Join Buzz Solutions and be part of a dynamic team that is shaping the future of energy and technology. If you are passionate about delivering exceptional customer support and thrive in a collaborative and innovative environment, we want to hear from you! Apply now to embark on an exciting journey with us.

  • Develop end-to-end backend pipelines for deploying machine learning models.
  • Build Docker containers for trained machine learning models.
  • Deploy Docker container images on Kubernetes engine.
  • Maintain Kubernetes engine and virtual machines on Google Cloud Platform.
  • Develop Flask and FastAPI frameworks in Python for deploying models as APIs.
  • Deploy the models in production as REST APIs on cloud infrastructure.
  • Observe and measure current product performance.
  • Review the process and product performance data with the team to develop standard work.
  • Document the process, code reviews and workflow to streamline product enhancements.
  • Develop end-to-end pipeline for deploying software platform backend infrastructure.
  • Build end-to-end methodology for backend modules of the cloud software platform.
  • Choose the optimal technology stack for building out the elements of the software platform backend.
  • Establish platform features and timelines for product roadmap.
  • Integrate SQL database objects with the software platform.
  • Integrate machine learning model REST API endpoints with software platform.
  • Deploy the platform in production on cloud infrastructure.
  • Work with a team of software engineers to enhance performance of the software platform and run continuous unit tests for deployed products.
  • Observe and measure current product performance.
  • Review the process and product performance data w/ team to develop standard work.
  • Document the process, code reviews and workflow to streamline product enhancements.
  • Maintain and monitor cloud infrastructure.
  • Monitor the logs of customer usage of the products and test for any vulnerabilities.
  • Maintain database systems and optimize for performance and costs.
  • Provide unit and stress testing frameworks for cloud infrastructure services deployed in production environments.
  • Document the process, code reviews and workflow to streamline product enhancements.

 

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