Senior Software Engineer (Lead Full Stack Engineer - AI). Contract Length: 6 Months with the view to extend - 3 - 5-year program of work Location: Sydney CBD, Australia Reports to: Senior Engineering Manager Immediate team: 1 x Senior Engineering Manager, 4 x Technical Leads, 10 x Software Engineers. Working arrangement: 3 days in office, flexible working hours. Role Purpose: The role also involves building agentic AI systems and contributing to a range of AI initiatives across distributed systems, cloud, and modern frontend technologies. You will join as a Senior Engineer (Lead Engineer) responsible for rebuilding and modernising a mission-critical insurance platform, with a strong focus on improving core architecture and refactoring legacy systems. What you will be responsible for: Break down monolithic legacy systems into scalable, cloud-native, autonomous services with resilience and self-healing capabilities Redesign and implement business rules, workflows, and UI behaviour using modern frameworks and architectures Build, validate, and refine AI-generated outputs such as code, specifications, tests, and workflows, ensuring correctness, safety, and performance Own end-to-end system behaviour including APIs, data flows, domain logic, and integrations, while applying secure-by-design principles and strong engineering patterns Your experience: Strong backend engineering expertise in Java (and ideally one additional ecosystem such as TypeScript or Python) with deep understanding of concurrency, performance, and distributed systems Design and build resilient Spring Boot microservices with a focus on scalability, observability, and reliability Develop modern frontend applications using React and TypeScript, with strong foundations in state management and API integration Design clean, versioned, contract-driven APIs and build robust testing strategies including integration, contract, and async system testing Evaluate and refine AI-generated code while simplifying complex legacy systems, improving architecture, and operating effectively across ambiguous, large-scale system design challenges
