Senior Platform Engineer, MLOps & ML Platform Lead

Commonwealth Bank; Sydney; Hybrid; AUD 180,000 - 240,000; Full time

Job summary

Commonwealth Bank seeks an experienced Staff Platform Engineer with MLOps expertise to build a secure, scalable ML platform for experiment to production. You will lead the evolution of platform capabilities, standards and paved roads enabling teams to productionise ML at scale.

You will work with data scientists and engineers to implement reusable infrastructure, training pipelines, model registry and compliant promotion paths, plus observability and governance across the lifecycle.

Qualifications

• Experience building ML platforms, MLOps capabilities or reusable engineering frameworks.
• Strong software engineering fundamentals with Python and automation.
• Understand ML lifecycle and collaborate with Data Scientists.

Responsibilities

• Provide technical leadership for ML platform engineering across teams.
• Own the paved road from data-science experiment to production, incl. training pipelines and model registry.
• Design reusable platform capabilities and tooling for productionising ML solutions.
• Develop AWS-based platform capabilities across batch inference, deployment and monitoring.
• Create infrastructure as code, CI/CD and automation with security controls.
• Ensure reproducibility, data/model/versioning and audit trails for regulatory review.
• Partner with Data Scientists to translate ML lifecycle needs into scalable platform features.
• Improve developer experience with self-service tooling and APIs.

Skills

Platform engineering

MLOps

Python

Automation

AWS

SageMaker

CI/CD

Docker

PySpark

Security

Tools

CloudFormation/CDK

Terraform

Terraform

Git

MLflow

Job description

Commonwealth Bank seeks an experienced Staff Platform Engineer with MLOps expertise to build a secure, scalable ML platform for experiment to production. You will lead the evolution of platform capabilities, standards and paved roads enabling teams to productionise ML at scale.

You will work with data scientists and engineers to implement reusable infrastructure, training pipelines, model registry and compliant promotion paths, plus observability and governance across the lifecycle.