Lead MLOps Platform Engineer

CommBank; Sydney; Híbrido; AUD 210.000 - 290.000; Jornada completa

Descripción de la vacante

CommBank is seeking a Staff Platform Engineer – MLOps to lead the design and evolution of scalable, secure ML platform capabilities that enable data scientists and engineers to move experiments to production safely and efficiently. You’ll drive reusable infrastructure, tooling, and best practices across teams.

You will collaborate with data scientists to translate ML lifecycles into robust platform features, improve self‑service tooling, and advocate for security, observability and compliance

Formación

• Extensive platform and MLOps experience across ML lifecycles.
• Strong Python and automation fundamentals.
• Experience with AWS ML services and CI/CD pipelines.
• Ability to collaborate with Data Scientists and engineers.

Responsabilidades

• Provide technical leadership for ML platform engineering across teams.
• Own data‑science to production roadmaps, training pipelines, model registry and promotion paths.
• Design reusable platform capabilities and tooling for productionising ML solutions.
• Engineer evolving AWS‑based platform capabilities across lifecycle: batch inference, deployment and monitoring.
• Build IaC, CI/CD and automation patterns with security and compliance controls.
• Ensure reproducibility, lineage and audit trails for model risk and regulatory review.
• Partner with Data Scientists to translate ML lifecycle requirements into scalable platform capabilities.
• Improve developer experience via self‑service tooling, libraries, APIs and paved roads.

Conocimientos

Platform engineering

Cloud engineering

DevOps

Infrastructure engineering

Python

Automation

Herramientas

AWS

SageMaker

S3

IAM

Glue

EMR

ECR

CloudWatch

Terraform

CDK

CloudFormation

Docker

PySpark

Descripción del empleo

CommBank is seeking a Staff Platform Engineer – MLOps to lead the design and evolution of scalable, secure ML platform capabilities that enable data scientists and engineers to move experiments to production safely and efficiently. You’ll drive reusable infrastructure, tooling, and best practices across teams.

You will collaborate with data scientists to translate ML lifecycles into robust platform features, improve self‑service tooling, and advocate for security, observability and compliance