As a Full Stack AI Engineer, you will be responsible for the end-to-end evolution of our core platform; 482 Visa sponsorship is available for the right candidate. You aren't just building interfaces; you are architecting an AI-native ecosystem. You will bridge the gap between sophisticated LLM integrations (OpenAI, Claude) and production-grade web applications, ensuring that AI features are not just prototypes, but scalable, reliable tools that drive user value. Core Responsibilities Platform Evolution: Build and evolve a full-stack platform, ensuring architectural integrity and scalability. Backend & API Design: Design robust backend services and APIs using Python (FastAPI) to handle complex logic and third-party integrations. Frontend Excellence: Develop seamless frontend experiences using modern JS frameworks to make AI interactions intuitive. LLM Integration: Architect and implement advanced workflows with OpenAI and Claude, focusing on RAG pipelines and agentic reasoning. Cloud Infrastructure: Manage and deploy services within AWS, ensuring high availability and cost optimisation. Rapid Prototyping: Transition quickly from high-fidelity prototypes to stable, production-ready features. Collaborative Delivery: Pair closely with Product Managers to define the roadmap and coach non-technical users on AI capabilities. Selection Criteria End-to-End Proficiency: Extensive experience across the entire stack, equally comfortable with database schema design and frontend state management. Delivery Track Record: A history of shipping real-world products to production in fast-paced environments. Engineering Rigour: Solid understanding of API design patterns, microservices, and distributed systems. AWS Capabilities: Practical experience with AWS (Lambda, ECS, RDS, S3). AI Enthusiasm: A deep interest in the evolving AI landscape, specifically around LLM orchestration and vector databases. The Ecosystem Languages & Frameworks: Python (FastAPI), Modern JS (React/Next.js). AI & Intelligence: OpenAI, Claude, LangChain/LlamaIndex. Data Tier: PostgreSQL, Redis, Databricks. Infrastructure: AWS (Full Suite), Docker, Git.