19 days ago
Sydney, Australia +4 moreSenior
Responsibilities
- Lead Azure discovery, advisory, and architecture engagements and translate customer objectives into secure platform designs.
- Design and implement Azure foundations including landing zones, identity, networking, governance, policy, security controls, and self-service capabilities.
- Build reusable automation using Infrastructure as Code, GitOps, policy as code, and continuous integration and delivery practices.
- Design and deploy AI-ready environments using Azure AI Foundry, Azure OpenAI, and Azure AI Search, including retrieval-augmented generation, vector search, and secure data integration.
- Develop and support governed agentic workflows with tool integrations, human oversight, lifecycle controls, testing, monitoring, and cost management.
- Establish responsible AI, observability, reliability, security, governance, and operational practices across cloud platforms, applications, AI services, and agentic workflows.
- Enable engineering teams through GitHub Enterprise, GitHub Actions, GitHub Advanced Security, GitHub Copilot, and secure AI-assisted software delivery.
- Contribute reusable architectures, automation, tools, delivery patterns, solution estimates, proposals, risk assessments, and customer presentations.
Requirements
- Proven experience in Azure consulting, cloud architecture, and customer-facing technology delivery.
- Strong hands-on cloud platform engineering and Infrastructure as Code experience using technologies such as Terraform or Bicep.
- Strong knowledge of secure-by-design cloud foundations, including identity, networking, governance, policy, security, and operational controls.
- Experience designing, implementing, or supporting enterprise AI platforms and AI-enabled solutions.
- Practical experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, or comparable enterprise AI platforms.
- Understanding of retrieval-augmented generation, vector search, secure data integration, model access controls, and responsible AI governance.
- Experience designing or supporting agentic workflows, AI automation, tool integrations, or AI-powered business processes.
- Understanding of enterprise AI and agentic solution security, governance, testing, observability, lifecycle, and cost considerations.
- Knowledge of modern developer tooling and delivery practices, including APIs, containers, test automation, CI/CD, GitOps, and secure software development.
- Fluency in at least one of Python, TypeScript, C#, Java, Go, or Rust, with the ability to design, interpret, and contribute to code-based solutions.
- Strong communication, stakeholder engagement, commercial awareness, and experience with workshops, solution shaping, estimates, proposals, or technical risk assessments.
- Desirable qualifications include advanced AI engineering, enterprise-scale AI application development, AI gateways, model observability, Model Context Protocol, Kubernetes platform engineering, FinOps, advanced security architecture, product development, software-as-a-service design, managed service transitions, relevant Azure, AI, GitHub, or Terraform certifications, mentoring, technical communities, or reference architecture ownership.
Benefits
- Hybrid workplace arrangement.
- Paid parental leave.
- 24/7 Employee Assistance Programme through DataCare.
- Ongoing learning, certifications, career development, industry-leading learning platforms, and future skills training.
- Recognition programmes, employee discounts, and financial wellbeing benefits.
- Meaningful work in an inclusive, collaborative environment; eligibility criteria and conditions may apply to some benefits.
Tech Stack
Categories
Solutions Engineering
