Posted:
3/2/2026, 12:46:01 PM
Location(s):
Sydney, New South Wales, Australia ⋅ New South Wales, Australia
Experience Level(s):
Senior
Field(s):
AI & Machine Learning ⋅ Software Engineering
You are an experienced MLOps Engineer who is passionate about machine learning platforms, automation, and cloud-native AI solutions.
We’re looking to set the standard for a world-class, self-service, secure, and scalable MLOps platform that enables rapid experimentation and safe production deployment of ML and Generative AI models.
Together, we can build state-of-the-art AI and LLM platforms that drive seamless, responsible experiences for millions of customers.
Do work that matters
Retail Banking Services (RBS) is the public face of CommBank, delivering a seamless banking experience for the future to our 10 million+ personal and small business customers. We offer market-leading products and services, supported by some of the world’s best systems, platforms, and engineering practices.
As a Senior MLOps Engineer, you will apply modern engineering and MLOps practices to operationalise machine learning and Large Language Models (LLMs) at scale. The role provides a strong opportunity to contribute to AI platform uplift, cloud adoption, and enterprise-grade ML enablement on AWS.
See yourself in our team
The RBS Customer Remediation crew is responsible for identifying root causes of issues, implementing control mitigations with Lines of Business, and ensuring customers are fully refunded for any bank error. We work closely with Group Customer Advocacy & Remediation (GCAR), Internal Audit, and Risk partners to deliver fair and positive outcomes for customers.
As part of this crew, the Senior MLOps Engineer will enable reliable, compliant, and observable ML solutions that support remediation analytics, automation, and decisioning use cases.
Your responsibilities will include:
Design, build, and maintain end-to-end MLOps pipelines for training, testing, deployment, and monitoring of ML and LLM models.
Operationalise models using AWS SageMaker, including training jobs, pipelines, model registry, batch inference, and real-time endpoints.
Support LLM and Generative AI workloads, including fine-tuning, inference optimisation, and deployment patterns.
Develop and maintain CI/CD pipelines for ML workflows and platform components.
Implement monitoring and observability for models and pipelines (data drift, model performance, system health).
Automate and improve ML development, release, and operational processes.
Take ownership of production support and technical troubleshooting for ML platforms and services.
Drive continuous improvement in platform reliability, security, cost, and performance.
Collaborate with data scientists and engineers to build robust ML pipelines that can handle large datasets and traffic.
Maintain security adherence and compliance standards, including data privacy and model explainability.
Ensure clear and comprehensive documentation of MLOps processes, infrastructure, along with configurations.
Participate as a senior member of the engineering team with minimal supervision and strong ownership and provide mentoring and technical assistance to other members of the team.
We’re interested in hearing from people who:
Have hands-on experience operationalising machine learning models in a cloud environment (AWS preferred).
Are passionate about MLOps, Cloud Engineering, Automation, and AI platforms.
Enjoy solving complex problems using a structured, engineering-led approach.
Have strong experience with Python and familiarity with SQL for data analysis and validation.
Are comfortable working in regulated, large-scale enterprise environments.
Technical Skills
We use a broad range of tools, languages, and frameworks. We don’t expect you to know them all, but experience with some of the following (or equivalents) will set you up for success:
Strong experience with AWS, particularly Amazon SageMaker
Experience with Infrastructure as Code (CloudFormation or Terraform)
Hands-on experience with core AWS services such as::S3, ECR, ECS, CloudWatch, KMS, secrets manager, Aurora DB, security groups
Experience supporting LLM or Generative AI workloads in production environments.
Strong programming and automation skills using Python
Experience with Docker and containerised ML workload
Hands-on experience with CI/CD tools (e.g. Git, GitHub Actions, Jenkins, TeamCity, Octopus, Artifactory) and
Understanding of DevOps and MLOps best practices including logging, monitoring, security, and reliability
Working with us
Whether you’re passionate about customer service, driven by data, or called by creativity, a career with CommBank is for you.
We support our people with the flexibility to balance where work is done with at least half your time each month connecting in our Sydney or Melbourne office. We also have many other flexible working options available including changing start and finish times, part-time arrangements and job share to name a few.
If this sounds like you, apply now!
If you're already part of the Commonwealth Bank Group (including Bankwest, x15ventures), you'll need to apply through Sidekick to submit a valid application. We’re keen to support you with the next step in your career.
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Website: https://commbank.com.au/
Headquarter Location: Sydney, New South Wales, Australia
Employee Count: 10001+
Year Founded: 1911
Last Funding Type: Post-IPO Debt
Industries: Banking ⋅ Finance ⋅ Financial Services ⋅ Wealth Management