Staff Platform Engineer - ML Ops

About the role

CommBank is looking for an experienced Staff Platform Engineer with MLOps experience to join its Economic Crime Technology team. The goal is to build a self-service, secure and scalable ML platform that lets Data Scientists and engineers experiment, train, deploy and operate ML solutions. You will provide technical leadership and engineering direction, shaping the platforms, frameworks and paved roads that let teams safely productionise ML at scale.

Key responsibilities

• Provide technical leadership and direction for ML platform engineering, influencing architecture, standards and engineering practices across teams.
• Own the path from data-science experiment to production, covering training pipelines, model registry, managed MLflow, the experimentation environment and a certified, reproducible promotion path, and extend it to onboard new model tenants and support retraining.
• Design and build reusable platform capabilities, frameworks and tooling for Data Scientists and engineers.
• Evolve AWS-based ML platform capabilities across the lifecycle, including batch inference/scoring, deployment and monitoring patterns.
• Build reusable Infrastructure as Code, CI/CD and automation patterns with security and compliance controls.
• Engineer reproducibility, lineage and audit-defensibility into the platform from day one, with versioned data, models and code, replayable runs and evidence trails for model-risk and regulatory review.
• Partner with Data Scientists to turn their workflow needs into scalable platform capabilities.
• Improve the developer and Data Scientist experience through self-service tooling, libraries, APIs and paved roads.

Required profile

• Deep experience in Platform, Cloud, DevOps or Infrastructure Engineering, with practical experience productionising machine learning workloads.
• Has designed or built ML platforms, MLOps capabilities or reusable engineering frameworks, rather than only consuming existing tooling.
• Strong software engineering fundamentals, particularly Python and automation.
• Understanding of the ML lifecycle and ability to work with Data Scientists without being one.

Required skills

• AWS, including SageMaker and services such as S3, IAM, Glue, EMR, ECR and CloudWatch.
• Python and reusable framework/library development.
• Infrastructure as Code with CloudFormation/CDK or Terraform.
• CI/CD and modern software delivery practices.
• Docker and containerised workloads.
• PySpark and distributed data processing.
• ML lifecycle capabilities: training pipelines, model registry, deployment and monitoring.
• Security, IAM, observability, resilience and production operations.
• Experience with Generative AI or LLM workloads is advantageous.