• You are an experienced Staff Platform Engineer with MLOps experience, passionate about building secure, scalable and reusable platforms that enable machine learning in production. 

• We’re looking to build a self-service, secure and scalable ML platform that enables Data Scientists and engineers to rapidly experiment, train, deploy and operate ML solutions. 

• You’ll provide technical leadership and engineering direction, shaping the platforms, frameworks and paved roads that enable teams to safely productionise ML at scale. 

Do work that matters 

Join Economic Crime Technology, where our crews build the detection, investigation and data capabilities that help protect customers and keep financial crime out of the bank. 

As a Staff Platform Engineer – MLOps, you will lead the design and evolution of the platform capabilities that enable machine learning solutions to move safely and efficiently from experimentation into production. 

See yourself in our team 

EC Technology brings together engineers, product professionals, data scientists and architects to strengthen the bank’s crime-detection, investigation and data platforms. 

As part of the crew, you’ll help shape the platform capabilities that enable reliable, compliant and observable ML solutions supporting remediation analytics, automation and decisioning. 

Your responsibilities will include 

• Provide technical leadership and direction for ML platform engineering, influencing architecture, standards and engineering practices across teams. 

• Own the paved road from data-science experiment to production, covering training pipelines, model registry, managed MLflow, the experimentation environment and a certified, reproducible promotion path. Extend it over time to onboard new model tenants and support retrains.  

• Design and build reusable platform capabilities, frameworks and tooling that enable Data Scientists and engineers to productionise ML solutions. 

• Engineer and evolve AWS-based ML platform capabilities across the lifecycle - batch inference/scoring, deployment, and monitoring patterns.  

• Build reusable Infrastructure as Code, CI/CD and automation patterns, incorporating security and compliance controls. 

• Engineer reproducibility, lineage and audit-defensibility into the platform from day one - versioned data/model/code, replayable runs, and evidence trails that satisfy model-risk and regulatory review. 

• Partner with Data Scientists to understand their workflows and translate ML lifecycle requirements into scalable platform capabilities. 

• Improve the developer and Data Scientist experience through self-service tooling, libraries, APIs and paved roads. 

We’re interested in hearing from people who 

• Have deep experience in Platform, Cloud, DevOps or Infrastructure Engineering, with practical experience productionising machine learning workloads. 

• Have designed or built ML platforms, MLOps capabilities or reusable engineering frameworks, rather than only consuming existing tooling. 

• Bring strong software engineering fundamentals, particularly Python and automation. 

• Understand the ML lifecycle and can work effectively with Data Scientists without needing to be a Data Scientist themselves. 

Technical skills 

We value engineering depth and transferable capability over experience with every specific technology. Experience with some of the following, or equivalent technologies, will set you up for success: 

• AWS, including SageMaker and services such as S3, IAM, Glue, EMR, ECR and CloudWatch 

• Python and reusable framework/library development 

• Infrastructure as Code using CloudFormation/ CDK or Terraform 

• CI/CD and modern software delivery practices 

• Docker and containerised workloads 

• PySpark and distributed data processing 

• ML lifecycle capabilities including training pipelines, model registry, deployment and monitoring 

• Security, IAM, observability, resilience and production operations 

• Experience supporting Generative AI or LLM workloads would be advantageous 

What success looks like 

You’ll create platform capabilities and engineering patterns that other teams can reuse, reducing the complexity of taking ML solutions into production while improving security, reliability and developer experience. 

Your impact will extend beyond what you personally build - you’ll raise engineering standards, influence technical direction and enable teams to deliver ML solutions safely and effectively at scale. 

Working with us  

Whether you’re passionate about customer service, driven by data, or called by creativity, a career with CommBank is for you.    

Our people bring their diverse backgrounds and unique perspectives to build a respectful, inclusive, and flexible workplace with flexible work locations. One where we’re driven by our values, and supported to share ideas, initiatives, and energy. One where making a positive impact for customers, communities and each other is part of our every day.   

Here, you’ll thrive. You’ll be supported when faced with challenges and empowered to tackle new opportunities. We’re hiring engineers from across all of our technology hubs in Sydney, Melbourne and Perth. We really love working here, and we think you will too.   

If this sounds like you, apply now!