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Machine Learning Operations Engineer (MLOps)

Roles & Responsibilities

  • Bachelor's degree in Computer Science or a related field with 4 years of relevant experience; Master’s degree with 2 years of relevant experience may be substituted for the Bachelor's plus 4 years.
  • Experience designing, implementing, and debugging web technologies and server architectures (Java, JavaScript, Node.js), and databases in cloud infrastructure with Python.
  • Experience developing applications and API integrations on cloud infrastructures to handle customer data.
  • Experience utilizing and maintaining Google Cloud Platform cloud infrastructure and services.

Requirements:

  • Develop end-to-end backend pipelines for deploying machine learning models and expose them as REST APIs on cloud infrastructure.
  • Build Docker containers for trained models and deploy container images on Kubernetes, while maintaining the Kubernetes engine and virtual machines on Google Cloud Platform.
  • Develop Flask and FastAPI frameworks in Python to serve models as APIs and deploy the platform backend with integrated ML endpoints; ensure production readiness with unit tests.
  • Observe and measure product performance, review performance data with the team to standardize workflows, and maintain cloud infrastructure including monitoring and cost optimization.

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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