Staff ML Platform Engineer - MLOps & Secure Self-Service

Narooma; Melbourne; Hybrid; AUD 180,000 - 240,000; Full time

Job summary

CommBank seeks an experienced Staff Platform Engineer – MLOps to lead the design and evolution of platform capabilities that move ML from experimentation to production. You will work with engineers, data scientists and architects to build reliable, compliant, observable ML solutions at scale.

You’ll own end-to-end ML infrastructure, focusing on training pipelines, model registry, and reproducible promotion paths, while expanding to onboard new tenants and retraining workflows.

Qualifications

• Experience in Platform, Cloud, DevOps or Infrastructure Engineering.
• Built ML platforms, MLOps capabilities or reusable frameworks.
• Strong Python and automation skills.

Responsibilities

• Provide technical leadership and direction for ML platform engineering across teams.
• Own the paved road from data-science experiment to production, including training pipelines and model registry.
• Design and build reusable platform capabilities and tooling for productionising ML solutions.
• Engineer AWS-based ML platform capabilities across lifecycle: batch inference, deployment, monitoring.
• Create Infra as Code and CI/CD patterns with security and compliance controls.
• Ensure reproducibility, lineage and audit-defensibility with versioned data/models/code.
• Collaborate with Data Scientists to translate ML lifecycle requirements into scalable platform features.
• Improve developer experience through self-service tooling and APIs.

Skills

Platform engineering

MLOps

Cloud

DevOps

Python

Automation

Security

Observability

Production ops

Tools

AWS SageMaker

Python

Terraform

CloudFormation/CDK

CI/CD

Docker

PySpark

CloudWatch

Job description

CommBank seeks an experienced Staff Platform Engineer – MLOps to lead the design and evolution of platform capabilities that move ML from experimentation to production. You will work with engineers, data scientists and architects to build reliable, compliant, observable ML solutions at scale.

You’ll own end-to-end ML infrastructure, focusing on training pipelines, model registry, and reproducible promotion paths, while expanding to onboard new tenants and retraining workflows.