Job Description

Staff Software Engineer – AI/ML, Payments Technology

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Staff Software Engineer – AI/ML, Payments Technology

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Sydney or Melbourne

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Build AI-powered engineering capabilities within one of Australia's largest and most critical Payments technology environments.

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Work at the intersection of software engineering, cloud and applied AI/ML, turning emerging AI capabilities into secure, scalable production systems.

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Shape how AI is engineered and adopted across Payments, creating reusable patterns, platforms and capabilities that can scale across teams.

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Do work that matters

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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.

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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.

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

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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.

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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.

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

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See yourself in our team

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

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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.

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You'll have the opportunity to:

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Design, build and operate production-grade AI/ML and GenAI solutions in a cloud environment.

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Build backend services and AI capabilities using Python and modern software engineering practices.

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Design and productionise AI agents and agentic workflows, including orchestration, tool use, grounding and human-in-the-loop patterns.

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Work with LLMs and cloud AI platforms to solve real engineering and operational problems within Payments.

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Develop RAG and grounding patterns, integrating models with enterprise data, APIs and internal systems.

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Establish approaches for evaluation, guardrails, observability and responsible AI, helping ensure AI solutions are reliable, explainable and appropriate for a regulated environment.

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Design reusable AI services, frameworks, APIs and platform capabilities that can be adopted across multiple engineering teams.

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Apply strong cloud-native engineering principles across scalability, resilience, security and performance.

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Champion DevSecOps and design‐to‐run ownership, taking solutions from architecture and development through deployment, observability and production operations.

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Explore how AI‐assisted engineering can improve software delivery, testing, operational support and developer productivity.

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Mentor engineers and help grow AI engineering capability across Payments Technology.

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Influence technical strategy and contribute to engineering standards and reference patterns for AI adoption across the organisation.

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We're interested in hearing from people who:

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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.

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You'll ideally:

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Bring strong Staff-level software engineering capability, with experience designing, building and operating complex production systems at scale.

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Have strong hands‐on programming capability, particularly in Python, with experience building production‐quality services and applications.

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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.

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Have practical experience building or productionising AI/ML or Generative AI solutions, rather than solely consuming AI tools.

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Understand modern AI engineering concepts such as LLM integration, agents, RAG, grounding, prompt engineering, tool use, evaluation and guardrails.

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Understand the challenges of taking AI from prototype to production, including scalability, latency, cost, security, observability and reliability.

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Bring experience designing APIs, microservices, distributed systems and event‐driven architectures.

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Have a strong DevSecOps mindset, with experience across CI/CD, automated testing, Infrastructure as Code, observability and production operations.

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Be comfortable making architectural decisions, evaluating trade‐offs and providing technical direction in ambiguous or rapidly evolving areas.

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Have experience mentoring engineers and influencing technical direction across teams.

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Technology & engineering workplace

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

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Exposure across a number of the following would be valuable:

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

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

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

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

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

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What great looks like

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You'll thrive in this role if you:

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Are a strong software engineer first, with genuine depth in AI/ML and cloud engineering.

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Enjoy turning emerging technologies into practical, production‐ready capabilities.

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Can move comfortably between experimentation, architecture and hands‐on engineering.

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Think beyond the model itself and consider the entire system around it — data, APIs, security, evaluation, observability, reliability and operations.

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Understand that building AI in a critical, regulated environment requires strong engineering discipline and responsible controls.

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Can take ambiguous problems and turn them into pragmatic technical solutions.

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Create reusable patterns and capabilities rather than solving the same problem independently in every squad.

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Influence engineers and technical leaders without needing direct authority.

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Stay curious and continuously experiment as AI engineering practices evolve.

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Care deeply about engineering quality, customer outcomes and building technology that can be trusted in production.

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Why Payments Technology?

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

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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.

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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.

📌 Staff Software Engineer - AI/ML, Payments Technology (Victoria)
🏢 Commbank
📍 Victoria