Cloud Native Machine Learning Platform Engineer

2 days ago

Sydney, New South Wales, Australia beBeeMachineLearning Full time $120,000 - $180,000

Job Overview

We are seeking a skilled Machine Learning Platform Engineer to develop, deploy, and manage end-to-end machine learning workflows in a scalable cloud environment.

The ideal candidate will have hands-on experience with AWS services, particularly SageMaker, Glue, EC2, Lambda, and CloudFormation, as well as proficiency in Python for automation and ML workflows.

Key Responsibilities:

  1. Design, develop, and manage ML workflows using Amazon SageMaker, AWS Glue, and Apache Airflow.
  2. Deploy and monitor scalable applications and ML models on Amazon EC2 and AWS Lambda.
  3. Automate infrastructure provisioning and resource management using AWS CloudFormation.
  4. Containerize applications using Docker and build robust CI / CD pipelines with GitHub Actions.
  5. Collaborate with data scientists, software engineers, and product teams to integrate ML models into production environments seamlessly.
  6. Write clean, efficient, and reusable Python code for automation, data processing, and orchestration.
  7. Ensure security, scalability, and reliability of all ML systems and cloud-based deployments in compliance with banking standards.
  8. Implement monitoring, logging, and alerting to ensure system health and performance.
  9. Drive best practices in MLOps, including version control, testing, and reproducibility of ML experiments.

Requirements and Qualifications

  • 4–7+ years of experience in MLOps, Cloud Engineering, or Machine Learning Engineering.
  • Strong hands-on experience with AWS services, particularly SageMaker, Glue, EC2, Lambda, and CloudFormation.
  • Proficiency in Python for automation and ML workflows.
  • Solid experience with Apache Airflow for workflow orchestration and data pipeline management.
  • Experience in Dockerizing applications and working with containerized environments.
  • Knowledge of CI / CD pipelines using GitHub Actions or similar tools.
  • Understanding of machine learning lifecycle, model deployment strategies, and production monitoring.
  • Familiarity with security best practices, especially in a regulated industry like financial services.

What We Offer

  • Opportunity to work on advanced ML infrastructure and cloud-native solutions.
  • Access to large-scale real-world ML problems and enterprise-grade tooling.
  • Work with a collaborative and innovative team at the forefront of cloud and AI technologies.
  • Access to ongoing learning and development resources.

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