Staff Platform Engineer

Commonwealth Bank; Melbourne; On-site; AUD 180,000 - 250,000; Full time

Job summary

Commonwealth Bank is seeking a Staff Platform Engineer to shape AI/ML platform capabilities, enabling teams to train, deploy and monitor models safely and efficiently. You’ll combine hands‑on engineering with leadership to deliver reusable MLOps solutions, production reliability and cost‑aware scalability.

This role sets engineering standards, guides platform roadmaps and reduces operational toil by embedding security and governance into platform design, while collaborating across security,

Qualifications

• Strong software engineering background with focus on Python and API development.
• Experience with cloud-native engineering, containers, Kubernetes and IaC.
• Proven ability to take AI/ML workloads into production, including model pipelines and monitoring.
• Ability to translate complex requirements into secure, reusable platform capabilities.
• Familiarity with production reliability, observability, and FinOps considerations.

Responsibilities

• Build and evolve AI/ML platform capabilities for training, evaluation, deployment, serving and monitoring of models in production.
• Embed versioning, traceability, automated checks, controlled promotion and rollback in deployment workflows.
• Drive automation and self-service, developing reusable infrastructure and deployment patterns.
• Lead operational improvements by analyzing incidents and performance trends to prevent repeat failures.
• Champion FinOps to reveal platform consumption and cost, guiding ownership decisions.
• Optimise workload scheduling, scaling, and resource utilisation balancing cost and resilience.
• Embed security and governance into platform design in collaboration with security and risk partners.
• Provide Staff-level technical leadership, shaping roadmaps and aligning teams around reusable solutions.

Skills

Python

API development

Automated testing

Maintainable tooling

Cloud-native engineering

Kubernetes

Containers

Infrastructure as code

AI/ML production

Model pipelines

Tools

Kubernetes

Docker

Terraform

Job description

• Build secure, scalable platforms that help teams take AI and machine learning capabilities from experimentation into production
• Drive automation, reliability and operational excellence, making platforms easier to use and simpler to operate
• Champion a FinOps mindset, balancing performance, resilience and cost to run efficient platforms at scale

• Build secure, scalable platforms that help teams take AI and machine learning capabilities from experimentation into production
• Drive automation, reliability and operational excellence, making platforms easier to use and simpler to operate
• Champion a FinOps mindset, balancing performance, resilience and cost to run efficient platforms at scale

Do work that matters

As a Staff Platform Engineer, you’ll shape and evolve the platform capabilities that enable teams to build, deploy and operate AI/ML workloads safely and efficiently. Combining deep technical expertise with hands‑on engineering leadership, you’ll turn complex platform challenges into reusable solutions. Your focus will span AI/ML Operations (MLOps), self-service automation, production reliability and resource efficiency, helping teams deliver faster without compromising security or operational control.

You’ll influence technical direction across teams, establish practical engineering standards and tackle the systemic issues that create operational toil, instability or unnecessary cost. This is a hands‑on technical leadership role, with impact measured through stronger platform capabilities, improved developer experience and sustainable operational outcomes.

You will

• Build and evolve AI/ML platform capabilities that support repeatable training, evaluation, deployment, serving and monitoring of models across their production lifecycle.
• Make secure delivery repeatable, embedding versioning, traceability, automated checks, controlled promotion and rollback into AI/ML deployment workflows.
• Drive automation and self-service, developing reusable infrastructure, deployment patterns and operational tooling that reduce manual effort and simplify platform adoption.
• Lead operational improvement, using incidents, performance trends and recurring support issues to address root causes and prevent repeat failures.
• Champion FinOps, making platform consumption and cost visible, strengthening ownership and helping teams understand the cost implications of engineering decisions.
• Optimise platform efficiency, improving workload scheduling, scaling, resource utilisation and capacity management while balancing cost, performance and resilience.
• Embed security and operational controls into platform design, working with security, architecture and risk partners to make compliant engineering practices easier to adopt.
• Provide Staff-level technical leadership, shaping platform roadmaps, leading complex design decisions and aligning teams around reusable solutions rather than fragmented implementations.

See yourself in our team

You’ll join a platform engineering team focused on making sophisticated technology easier to build on and safer to operate. We treat platforms as products, with clear ownership, reusable capabilities and a strong understanding of the teams who depend on them. You’ll work closely with software engineers, AI/ML practitioners, architecture, security and operations to create practical solutions that meet production needs.

You’ll bring a balance of strategic thinking and hands‑on delivery, knowing when to establish a shared pattern, when to simplify an existing solution and when to challenge complexity that does not deliver value.

We’re interested in hearing from people who have the following

Platform Engineering and AI/MLOps

• Strong software engineering skills, including Python or another relevant programming language, API development, automated testing and maintainable tooling.
• Deep understanding of cloud‑native engineering, containers, Kubernetes, infrastructure as code and automated delivery practices.
• Practical experience taking AI/ML workloads into production, including model pipelines, artefact and version management, deployment, serving and monitoring.
• Understanding of the operational needs of AI/ML workloads, including reproducibility, data and model dependencies, compute requirements and production performance.
• Ability to translate complex requirements into secure, reusable platform capabilities that engineers can adopt with minimal friction.

Reliability, Automation and FinOps

• Strong experience applying reliability engineering practices, including service-level objectives, observability, capacity planning, incident response and recovery.
• An automation-first mindset, with demonstrated experience eliminating recurring manual work and improving developer and operator experience.
• Experience applying FinOps principles, including cost allocation, consumption visibility, rightsizing and workload optimisation.
• Ability to assess cost, performance and resilience trade-offs, including the resource demands of AI/ML training and inference.
• Experience embedding security, access controls and operational governance into engineering workflows.
• Strong technical judgement and the ability to influence across teams, mentor engineers and turn engineering improvements into measurable outcomes.

Working with us

At CommBank, we’re committed to creating an accessible, inclusive and respectful workplace. If you require support or adjustments, please let us know. We welcome applications from people of all backgrounds and we’re particularly committed to making a positive difference for Aboriginal and/or Torres Strait Islander Peoples.

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