Role Overview

Trilogy Care helps older Australians take control of the care they receive at home through AI, automation, and modern data platforms that connect funding, services, and providers.

AI is already central to how we operate, with LLMs, intelligent document processing, agentic workflows, and real-time data pipelines driving our platform. We’re looking for an AI Engineer to join our small, high-autonomy team and work closely with our Senior leaders to design, build, and deploy production AI solutions across cloud infrastructure, data pipelines, and LLM-powered applications.

This is a hands-on role with real ownership, where you’ll solve business problems end-to-end using the right mix of AI, cloud, and software engineering. 

What You’ll Do

• Design, develop, and maintain AI-powered solutions, including LLM applications, automation tools, intelligent document processing, and agentic workflows.
• Build and support scalable cloud infrastructure, data pipelines, and integrations within AWS to enable reliable AI and business applications.
• Deliver end-to-end solutions, from requirements gathering and solution design through to development, deployment, optimisation, and ongoing support.
• Develop and integrate advanced AI capabilities, including RAG solutions, prompt engineering, model orchestration, and API-driven applications.
• Ensure all solutions align with security, privacy, governance, and compliance requirements, particularly when handling sensitive customer data.
• Collaborate closely with our Senior leaders and cross-functional stakeholders to translate business challenges into practical, high-impact technology solutions.
• Stay current with emerging AI technologies and contribute to continuous improvement, knowledge sharing, and engineering best practices across the team.

What You’ll Bring

•  AI/LLM Engineering (3–5 years). Demonstrated experience building production AI systems - LLM applications, agentic workflows, RAG, NLP, intelligent document processing, or similar. Not just prototypes - shipped, maintained, production systems.
• Cloud Infrastructure (AWS). Hands-on experience designing and operating cloud-native infrastructure - ECS/Fargate, Lambda, S3, API Gateway, IAM, networking. At least 2 years in AWS.
• Software Engineering. Strong Python skills (TypeScript/Node.js also valued). Clean code, version control, CI/CD, testing, and production debugging. Infrastructure-as-code is second nature.
• System Design. Ability to architect scalable, secure systems - API design, event-driven patterns, data pipelines, and integration architecture. You think about failure modes, not just happy paths.
• Security Mindset. You've built systems handling sensitive data. Authentication, authorisation, encryption, and audit controls are part of how you think - not an afterthought.
• Autonomy & Ownership. You take ambiguous problems and deliver complete solutions without hand-holding. Self-directed, proactive, and accountable for outcomes.
• Communication. Able to explain technical work clearly to both technical and non-technical audiences. Written and verbal.

Qualifications

• No formal degree requirement - equivalent experience and demonstrated capability will be considered equally.
• AWS certifications, such as Solutions Architect, Developer, or Machine Learning Specialty, are highly regarded.
• Relevant AI/ML certifications or completion of advanced coursework are desirable.
• Demonstrated commitment to continuous learning and professional development within AI and machine learning, including publications, open-source contributions, personal projects, or similar initiatives.