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Cloud-Focused Machine Learning Operations Engineer

2 weeks ago

Sydney, New South Wales, Australia beBeeMachine Full time $180,000 - $200,000
Key Skills for a Cloud-First Machine Learning Platform

The role of an ML Ops Engineering Lead involves leveraging technical expertise to design, develop and manage machine learning workflows within scalable cloud environments.

This requires proficiency in Python, with hands-on experience in automation, data processing, and orchestration. Strong familiarity with AWS services is also essential, particularly SageMaker, Glue, EC2, Lambda, and CloudFormation.

Collaboration and communication skills are vital in working effectively across engineering, data, and business teams. Additionally, knowledge of security best practices, especially in regulated industries like financial services, is crucial.

  • Design and Develop ML Workflows:
  • Develop, deploy, and manage end-to-end machine learning workflows in Amazon SageMaker, AWS Glue, and Apache Airflow.
  • Deploy Scalable Applications:
  • Deploy and monitor scalable applications and machine learning models on Amazon EC2 and AWS Lambda.
  • Automate Infrastructure Provisioning:
  • Automate infrastructure provisioning and resource management using AWS CloudFormation.
  • Containerize Applications:
  • Containerize applications using Docker and build robust CI/CD pipelines with GitHub Actions.
  • Model Integration:
  • Collaborate with data scientists, software engineers, and product teams to integrate machine learning models into production environments seamlessly.
  • Security and Compliance:
  • Ensure the security, scalability, and reliability of all machine learning systems and cloud-based deployments in compliance with banking standards.
  • Monitoring and Logging:
  • Implement monitoring, logging, and alerting to ensure system health and performance.
  • Best Practices in MLOps:
  • Drive best practices in MLOps, including version control, testing, and reproducibility of machine learning experiments.