Staff Software Engineer- AI/ML, AI CoE Payments Technology

Staff Software Engineer – AI/ML, Payments Technology

Sydney or Melbourne

• Build AI-powered engineering capabilities within one of Australia's largest and most critical Payments technology environments.
• Work at the intersection of software engineering, cloud and applied AI/ML, turning emerging AI capabilities into secure, scalable production systems.
• Shape how AI is engineered and adopted across Payments, creating reusable patterns, platforms and capabilities that can scale across teams.

Do work that matters

Payments Technology sits at the heart of CommBank, building and operating platforms that move billions of dollars and support millions of customers and businesses every day.

We're investing in an AI Centre of Excellence within Payments Technology and are looking for several Staff Software Engineers to help us shape the next generation of AI-enabled engineering and payment capabilities.

This isn't about experimentation for experimentation's sake.

You'll work on practical applications of AI across Payments — from engineering productivity and intelligent automation through to agentic workflows, operational processes, investigation and exception handling.

As a Staff Software Engineer, you'll combine strong software engineering fundamentals with hands-on AI/ML and cloud capability to take ideas from experimentation through to secure, reliable and observable production systems.

You'll also help establish the engineering patterns, guardrails and reusable capabilities that enable other Payments teams to adopt AI safely and effectively.

See yourself in our team

You'll join the Payments Engineering AI Centre of Excellence, working alongside engineers, architects, product teams and specialists across the broader Payments organisation.

You'll operate as a senior technical leader — remaining hands-on while influencing architecture, engineering standards and the way teams design, build and operate AI-enabled software.

You'll have the opportunity to:

• Design, build and operate production-grade AI/ML and GenAI solutions in a cloud environment.
• Build backend services and AI capabilities using Python and modern software engineering practices.
• Design and productionise AI agents and agentic workflows, including orchestration, tool use, grounding and human-in-the-loop patterns.
• Work with LLMs and cloud AI platforms to solve real engineering and operational problems within Payments.
• Develop RAG and grounding patterns, integrating models with enterprise data, APIs and internal systems.
• Establish approaches for evaluation, guardrails, observability and responsible AI, helping ensure AI solutions are reliable, explainable and appropriate for a regulated environment.
• Design reusable AI services, frameworks, APIs and platform capabilities that can be adopted across multiple engineering teams.
• Apply strong cloud-native engineering principles across scalability, resilience, security and performance.
• Champion DevSecOps and design-to-run ownership, taking solutions from architecture and development through deployment, observability and production operations.
• Explore how AI-assisted engineering can improve software delivery, testing, operational support and developer productivity.
• Mentor engineers and help grow AI engineering capability across Payments Technology.
• Influence technical strategy and contribute to engineering standards and reference patterns for AI adoption across the organisation.

We're interested in hearing from people who:

We don't expect you to have experience with every technology or AI framework listed below. We're looking for strong engineers with depth across software engineering, cloud and AI/ML who are comfortable learning quickly as the technology continues to evolve.

You'll ideally:

• Bring strong Staff-level software engineering capability, with experience designing, building and operating complex production systems at scale.
• Have strong hands-on programming capability, particularly in Python, with experience building production-quality services and applications.
• Have strong experience with cloud-native engineering. We primarily use AWS, but experience designing and operating sophisticated solutions on Azure or GCP is also valuable, provided you're comfortable adapting to AWS.
• Have practical experience building or productionising AI/ML or Generative AI solutions, rather than solely consuming AI tools.
• Understand modern AI engineering concepts such as LLM integration, agents, RAG, grounding, prompt engineering, tool use, evaluation and guardrails.
• Understand the challenges of taking AI from prototype to production, including scalability, latency, cost, security, observability and reliability.
• Bring experience designing APIs, microservices, distributed systems and event-driven architectures.
• Have a strong DevSecOps mindset, with experience across CI/CD, automated testing, Infrastructure as Code, observability and production operations.
• Be comfortable making architectural decisions, evaluating trade-offs and providing technical direction in ambiguous or rapidly evolving areas.
• Have experience mentoring engineers and influencing technical direction across teams.

Technology & engineering environment

Our environment continues to evolve, so we're more interested in strong fundamentals and adaptability than experience with every individual technology.

Exposure across a number of the following would be valuable:

AI & ML Engineering
Generative AI, LLMs, AI agents, agentic workflows, RAG, prompt engineering, model evaluation, grounding, guardrails, responsible AI and human-in-the-loop patterns.

Cloud & AI Platforms
AWS, Amazon Bedrock and cloud-native architectures. Relevant Azure or GCP AI/cloud experience is also welcomed.

Software Engineering
Python, Java, Node.js/TypeScript, APIs, microservices and distributed systems.

Data & Event-Driven Engineering
Kafka/event streaming, data pipelines, vector and relational data stores, schema management and integration patterns.

Platform & DevSecOps
Infrastructure as Code, Terraform, Kubernetes/containers, CI/CD, GitHub Actions, observability, security and automated engineering controls.

What great looks like

You'll thrive in this role if you:

• Are a strong software engineer first, with genuine depth in AI/ML and cloud engineering.
• Enjoy turning emerging technologies into practical, production-ready capabilities.
• Can move comfortably between experimentation, architecture and hands-on engineering.
• Think beyond the model itself and consider the entire system around it — data, APIs, security, evaluation, observability, reliability and operations.
• Understand that building AI in a critical, regulated environment requires strong engineering discipline and responsible controls.
• Can take ambiguous problems and turn them into pragmatic technical solutions.
• Create reusable patterns and capabilities rather than solving the same problem independently in every squad.
• Influence engineers and technical leaders without needing direct authority.
• Stay curious and continuously experiment as AI engineering practices evolve.
• Care deeply about engineering quality, customer outcomes and building technology that can be trusted in production.

Why Payments Technology?

Few engineering environments combine the scale and criticality of Payments with the opportunity presented by AI.

You'll have the opportunity to help shape how one of Australia's largest technology organisations applies AI to real-world engineering and payment challenges — while working on systems where security, reliability and engineering excellence genuinely matter.

If you're an experienced engineer who enjoys building at scale and wants to help move AI from possibility to production, we'd love to hear from you.

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