Cloud Architect
HybridBrisbane, Queensland, Australia
Job Summary
Design end-to-end cloud architecture for a new SaaS medical software platform, defining principles, patterns, and standards that guide development while ensuring scalability, security, and compliance with Australian healthcare regulations. Stay embedded in delivery to attend technical reviews, sprint ceremonies, and design sessions, working directly with developers to solve complex problems, validate implementations, and contribute to infrastructure-as-code and CI/CD pipeline design. Produce detailed architecture documentation, decision records, and artefacts that support multi-tenancy, availability, and cost efficiency. Embed security into the platform from the ground up, covering identity, access, encryption, and audit logging, and collaborate with stakeholders to validate decisions against regulatory obligations. Architect infrastructure capable of supporting AI and ML workloads, including compute, storage, and model serving, while addressing latency, cost, and explainability requirements.
Required Qualifications
- Deep, hands-on Azure expertise — you've designed and built production-grade cloud platforms, not just drawn the diagrams
- Proven experience architecting SaaS platforms from greenfield — you understand what multi-tenancy, scalability, and cloud-native design actually look like in practice
- Strong command of Azure-native services — App Service, AKS, Azure SQL, Service Bus, Key Vault, Azure Monitor, and the broader ecosystem
- Solid understanding of infrastructure-as-code (Terraform, Bicep, or ARM) and modern CI/CD practices
- Experience designing for security and compliance in regulated industries — you understand what it means to build a platform that handles sensitive data responsibly
- The ability to be both the big-picture thinker and the person in the weeds when it matters
- Strong communication skills — able to articulate complex architectural decisions to developers, product leaders, and executives equally well
- Experience architecting platforms that host or serve LLM or ML workloads
- Familiarity with MLOps patterns on Azure
- TGA or FDA perspective on AI as a medical device
- Azure AI / OpenAI service integration experience
- Experience with Azure AI services (Azure OpenAI, Cognitive Services, ML Studio) and the architectural patterns that support them at scale
- Understanding of the infrastructure implications of embedding AI into a safety-critical SaaS product — latency, cost, auditability, and explainability requirements
- Awareness of AI governance and risk management considerations specific to medical software and patient data
- Comfortable using AI-assisted tooling in your own workflow — infrastructure design, documentation, threat modelling, or cost optimisation
Desired Qualifications
- Experience architecting platforms that host or serve LLM or ML workloads
- Familiarity with MLOps patterns on Azure
- TGA or FDA perspective on AI as a medical device
- Azure AI / OpenAI service integration experience
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