About
About Me
Senior Software Engineer with 14+ years of experience in distributed systems, microservices architecture, and event-driven platforms across banking and public infrastructure. Now at the forefront of AI-native engineering — designing, orchestrating, and validating AI-generated solutions.
Skills
AI & Tooling: Claude Code, GitHub Copilot, AI-augmented development workflows
Cloud & Azure: App Service, Azure Functions, Service Bus, Application Insights
Backend: Java, C#, TypeScript, Spring Boot, Kafka, REST/GraphQL APIs
Security: OAuth2, OIDC, mTLS, API Gateway patterns, Open Banking standards
DevOps: GitHub Actions, CI/CD, Doc-as-Code, Infrastructure as Code
Architecture: CQRS, Saga, Circuit Breaker, Event-Driven, Finite State Machine
Experience
Software Engineer — WiseTech Global
Melbourne, VIC
Working in an AI-augmented engineering model within the Australian logistics domain, contributing to Transport Management System (TMS) optimisation and cloud-native platform development.
- Adopted an AI-native development approach using Claude Code and GitHub Copilot, acting as a technical control layer responsible for problem decomposition, AI orchestration, and ensuring production-grade quality of generated solutions
- Applied Vehicle Routing Problem (VRP) techniques to improve route efficiency, dynamic dispatching, and real-time decision-making in a logistics TMS
- Designed and maintained Azure-based event-driven services using App Service, Azure Functions, and Service Bus to build scalable, reliable architectures
- Implemented system observability and diagnostics with Azure Application Insights and KQL, enabling proactive monitoring and faster incident resolution
- Built and maintained GitHub Actions CI/CD pipelines to streamline multi-environment deployments
- Championed a Doc-as-Code approach, version-controlling technical documentation alongside source code to improve team alignment and knowledge retention
- Practised Test-Driven Development (TDD) in C# to ensure production-grade reliability, maintainability, and confidence in AI-generated code outputs