Staff Software Engineer - AI Powered Engineering team

Staff Software Engineer – AI-Powered Engineering team

Location: Sydney or Melbourne

Engineer the Future of AI-Augmented Software Delivery at Enterprise Scale

At CommBank, we are building the next generation of AI-powered engineering platforms that materially improve how thousands of engineers design, build, test, secure, and operate software.

This role sits at the core of that transformation.

We are not building prototypes or isolated experiments. We are engineering production-grade AI capabilities that integrate directly into CI/CD pipelines, SDLC workflows, developer tooling, and platform services — safely, measurably, and at enterprise scale.

If you are a deeply technical software engineer who can design distributed systems, influence architecture across domains, and pragmatically operationalise AI coding agents such as Claude Code or GitHub Copilot in real delivery environments, this role is for you.

Do Work That Matters

The AI Powered Engineering function exists to:

• Embed AI-assisted capabilities across the SDLC
• Improve developer experience and engineering flow efficiency
• Increase delivery velocity without compromising security or compliance
• Maintain high standards of reliability, auditability, and operational excellence

In this role, you will design and build the infrastructure, guardrails, integrations, and automation layers that make AI usable and safe inside a highly regulated enterprise environment.

About the Team

We are a product-minded engineering team operating at the intersection of:

• Platform Engineering
• Developer Productivity & Flow Engineering
• Applied AI & LLMOps
• Secure Software Supply Chain
• AI-Assisted SDLC Automation

We experiment, validate, and scale capabilities such as:

• AI-powered coding assistants (e.g. Claude Code, GitHub Copilot, Patchwork, Roo Code)
• Internal knowledge copilots grounded in engineering, architecture, and compliance artefacts
• Automated generation and validation of API documentation, control evidence, and compliance workflows
• AI-augmented incident triage and operational support in partnership with Cyber and SRE teams
• AI-driven code review augmentation and secure code remediation workflows

Everything we build must meet enterprise standards for:

• Security
• Auditability
• Observability
• Data governance
• Measurable engineering impact

What You’ll Do

As a Staff Software Engineer, you will operate as a senior technical authority and multiplier across the organisation.

Architecture & Platform Engineering

• Design and implement resilient, reusable services that integrate AI capabilities into SDLC tooling, CI/CD pipelines, and developer platforms.
• Architect event-driven, API-first systems that support scalable AI-assisted workflows.
• Build middleware, orchestration layers, and guardrails for safe LLM integration.
• Engineer secure abstraction layers around Claude Code, GitHub Copilot, and other AI coding agents.
• Develop high-throughput, low-latency services capable of supporting enterprise-scale engineering workloads.

AI-Augmented Engineering Enablement

• Evaluate, benchmark, and productionise AI coding agents (e.g., Claude Code, Copilot) for enterprise usage.
• Design prompt orchestration, grounding strategies, and evaluation frameworks.
• Integrate AI agents into:
  • Code generation workflows
  • Test generation pipelines
  • Static analysis and remediation loops
  • Pull request review automation
  • Compliance evidence generation
• Develop measurable experimentation frameworks to quantify productivity uplift, code quality impact, and risk exposure.
• Implement deterministic validation, fallback mechanisms, and output verification patterns around probabilistic AI systems.

DevSecOps & Secure SDLC

• Embed AI into secure supply chain workflows while maintaining:
  • SAST / DAST / SCA enforcement
  • Secrets detection and protection
  • SBOM generation
  • Policy-as-code guardrails
• Design controls to ensure AI outputs meet regulatory and security standards.
• Partner with Cyber and Risk teams to operationalise AI responsibly.
• Ensure traceability and auditability of AI-assisted changes across the delivery lifecycle.

Engineering Leadership

• Drive technical design from discovery through to production.
• Produce high-quality design documents, RFCs, and architecture diagrams.
• Influence standards across a broad stakeholder landscape (50+ teams).
• Mentor engineers and uplift AI literacy and engineering craft.
• Role-model high engineering standards in testing, documentation, and review practices.

What You Bring

- Deep Software Engineering Expertise

• Extensive experience building and operating distributed systems in production.
• Proficiency in one or more of: Python, Java, C#, TypeScript, Node.js.
• Strong understanding of:
  • Microservices architectures
  • Event-driven systems (Kafka, SNS/SQS, Pub/Sub, etc.)
  • API design (REST, gRPC)
  • Data modelling and SQL/NoSQL systems
  • Concurrency, performance, and scalability design patterns

- Cloud & Platform Engineering

Experience designing cloud-native systems on AWS, Azure, or GCP, including:

• Compute (Lambda/Functions, ECS/EKS/AKS, Kubernetes)
• API Gateways
• Step Functions / Durable Workflows
• Managed databases
• IAM and fine-grained access control

Infrastructure and automation capability:

• Infrastructure as Code (Terraform, ARM, CloudFormation)
• CI/CD pipelines (GitHub Actions, GitLab, Azure DevOps)
• Containerisation (Docker, Kubernetes)
• Platform observability (OpenTelemetry, Prometheus, Grafana, ELK)

AI Coding Agent & LLM Integration Capability

Hands-on experience with, or strong working knowledge of:

• AI coding assistants such as Claude Code, GitHub Copilot, or equivalent
• LLM API integration and orchestration
• Prompt engineering and evaluation strategies
• Retrieval-Augmented Generation (RAG) concepts
• Guardrails, rate limiting, and safe usage patterns
• Experiment design and AI capability benchmarking
• AI output validation and risk mitigation patterns

You understand that AI agents are probabilistic systems and know how to design deterministic wrappers, validation layers, and fallback mechanisms around them.

DevSecOps & Secure Engineering

• Secure software supply chain practices (SAST/DAST/SCA)
• Secrets management and key rotation
• Policy-as-code enforcement
• Threat modelling and secure design principles
• Auditability and traceability of automated workflows

Why This Role Is Different

You will not be building isolated AI features.

You will be engineering the platform capabilities that determine how AI is safely used by thousands of engineers across one of Australia’s most complex technology environments.

This is a rare opportunity to define what responsible, enterprise-scale AI-assisted engineering looks like in practice.

If this sounds like you, apply today!