Do work that matters:

Help build tomorrow’s bank today by delivering cutting-edge Generative AI that transforms how millions of customers and colleagues experience financial services. As a Staff Software Engineer – Chapter Lead – Generative AI, you’ll drive the AI and strategic technology roadmap, shaping how GenAI is applied at scale across CommSec. This is a hands-on leadership role where you’ll build production-grade capabilities, support groups and functions in unlocking AI value, and lead a team of highly specialised senior engineers through CommSec’s Generative AI journey.

You’ll set enterprise standards with strong foundations in MLOps, evaluation, observability, and security, while fostering a culture of inclusion, ownership, and technical excellence. The impact is real, accelerating your career while delivering meaningful innovation, improving customer outcomes, and helping shape Australia’s future economy. 

This role is for a technical leader who is hands‑on in the code, builds real solutions, solves complex problems, and leads a high‑performing team of senior engineers.

See yourself in our team:  

Within CommSec, the CommSec Technology Platform Crew sits within the broader CommSec Technology division, part of the Business Banking Technology domain, operating at the intersection of trading, data, and advanced AI. The team builds intelligent systems that directly shape how Australians invest and manage wealth, delivering production-grade AI, Generative AI, and agentic solutions that are deeply embedded into the CommSec ecosystem.

Their work spans across Retrieval Augmented Generation (RAG), agentic workflows, and real-time integrations with trading and customer platforms. They apply machine learning, NLP, and advanced analytics to solve complex, real-world problems, from generating market insights to automating sophisticated workflows.

With a strong engineering discipline, the team drives CI/CD, infrastructure as code, observability, and security-first practices to ensure systems are reliable, scalable, and production-ready, shaping how Generative AI is applied in a high-impact, regulated environment.

We’re interested in hearing from people who: 

• Build and scale production-grade GenAI platforms (LLMs, RAG, agentic workflows) by creating reusable frameworks and integrating them into existing enterprise systems, while leading engineers to make pragmatic architecture decisions and align delivery with real-world business problems

• Translate advanced ML and NLP capabilities into deployable solutions by guiding teams through design trade-offs (latency, cost, accuracy), ensuring engineers understand both the underlying models and how they operate in production environments

• Establish end-to-end AI quality and safety practices (evaluation frameworks, LLM observability, bias/risk controls) and lead teams to adopt data-driven validation approaches, embedding accountability and engineering rigor in day-to-day development

• Drive strong DevSecOps maturity (CI/CD, IaC, automated testing, monitoring, cost optimisation) by setting engineering standards, enabling teams to ship reliably at scale, and coaching them on production ownership including on-call and incident response

• Champion automation-first engineering culture across the AI lifecycle, reducing manual overhead while empowering teams to focus on higher-value problem solving and continuous improvement

• Lead, mentor, and grow high-performing engineering teams by fostering autonomy, psychological safety, and constructive challenge, ensuring individuals build deep expertise in both AI technologies and scalable system design

• Communicate effectively with senior stakeholders by translating complex AI concepts into clear trade-offs, guiding teams to balance innovation with risk, and aligning technical execution with strategic business outcomes

Tech skills 

We don't expect you to know everything, but experience in several of the following will set you up for success:

• Programming Language: Python

• AI/ML Frameworks: LangChain, LangGraph, LlamaIndex, AutoGen

• Generative AI: Agentic frameworks, RAG, Model Evaluation

• Vector Databases: Pinecone, Weaviate, Opensearch

• ML Engineering: Model Training/Inference Pipeline/Orchestration development

• MLOps: Model lifecycle management, Model Serving/Performance Observability

• Cloud Platform: AWS services

• Infrastructure as Code: CloudFormation, CDK, Terraform

• Containerisation: Docker, Kubernetes

• CI/CD: GitHub Actions, GitLab CI, Jenkins

• AI Evaluation: Evaluation frameworks from traditional Machine Learning model to GenAI workflow/objects

• Monitoring: Evidently AI, Datadog

• Observability & Reliability Engineering: Observe, Prometheus, Grafana, PagerDuty, CloudWatch

• Security: AI/ML system security

• Governance: AI governance frameworks