Great things start here

At RACV, you’ll work on meaningful AI problems in a trusted organisation with real customer, operational and employee use cases. You’ll have the autonomy to own features end-to-end while working alongside a high calibre AI and technology team that cares about shipping well, not just experimenting.

We’re looking for an AI Engineer who can independently design, build, deploy and improve LLM centric and agentic AI systems, from retrieval and orchestration through to evaluation, security and reliable production operation. This is a hands-on role for someone who wants to turn modern AI capability into dependable production software and who brings strong judgement in system design for scalable, high-quality services.

About the opportunity

As an AI Engineer, you’ll help shape and deliver RACV’s AI platforms, software and infrastructure. Working with minimal day to day supervision, you’ll own discrete workstreams and contribute to the technical quality of production AI solutions from design through to release and iteration.

You’ll work hands on across RAG, agentic workflows, tool use, multi-step reasoning, prompt and context engineering, while balancing the realities of running AI in production, including cost, latency, scalability, observability, system reliability and safety. From day one, you’ll embed security and responsible AI practices aligned to RACV governance expectations.

This role will suit an engineer who enjoys combining strong software fundamentals with fast moving AI techniques, and who wants to work where production quality, governance and practical business impact all matter.

What you’ll do

Reporting to the Lead AI Full Stack Engineer, you’ll deliver production grade AI solutions, including:

• Build, deploy and maintain AI enabled applications, services and APIs used in real production environments

• Design and implement LLM systems including RAG pipelines, agentic workflows, orchestration and context strategies

• Develop AI pipelines, including serverless ingestion, transformation and analysis workflows across different data sources

• Design systems and services that scale reliably in production, with clear attention to architecture, performance, fault tolerance and maintainability

• Own prompt and context engineering for production, including prompt libraries, retrieval strategies and iteration cycles

• Design and run structured LLM evaluation across accuracy, hallucination, relevance, safety, tool behaviour and other quality signals that matter in production

• Define release readiness using evaluation baselines, acceptance thresholds, tracing and production feedback loops

• Write production quality Python and TypeScript/JavaScript for applications, APIs and microservices

• Deliver AI securely and responsibly, including controls around prompt injection, PII handling, audit logging and third party model risks

• Design appropriate guardrails, approval steps and fallback paths for higher risk or higher impact workflows

• Maintain reliable systems via CI/CD pipelines, containerised deployments (Docker/Kubernetes) and monitoring

• Collaborate closely with data scientists, ML specialists, analytics, DevOps and Cybersecurity teams to move good ideas into dependable delivery

  
  

What you’ll bring

You do not need to match every tool or framework we use today. We’re most interested in engineers with strong production instincts, sound judgement and hands on experience across most of the areas.

• 3–5 years’ experience in software engineering with a strong focus on AI and LLM technologies

• Hands on experience building production systems using foundation models, RAG and agentic patterns

• Strong production experience designing and operating systems at scale, with good system design fundamentals across reliability, performance and maintainability

• Experience designing or operating data ingestion and transformation pipelines in a cloud environment

• Experience with agent frameworks such as LangGraph, OpenAI Agents SDK, Microsoft Agent Framework, LlamaIndex or PydanticAI, or the ability to get productive quickly with equivalent tools

• Practical experience running LLM evals and using tracing or observability to improve production quality

• Strong prompt and context engineering capability for production workloads

• Production level Python and TypeScript/JavaScript

• Experience with APIs, microservices, CI/CD and container platforms

• Working knowledge of cloud platforms (Azure preferred)

• Applied understanding of responsible AI, AI ethics and AI specific security risks

  
  

Qualifications (desirable)

• Degree in Computer Science, Cloud Computing, Mathematics or related field (or equivalent experience)

• Experience with Azure Functions, Azure Durable Functions or similar serverless orchestration patterns

• Azure/AWS certifications

• Experience with LLM observability tools (e.g. LangSmith, Langfuse) or OpenTelemetry based tracing

• Open-source contributions or exposure to ML training/fine tuning pipelines

  
  

What’s in it for you

• The chance to help shape how AI is built, deployed and governed in production at RACV

• High trust, high autonomy work with meaningful end to end ownership

• The opportunity to work alongside experienced AI, engineering, data and security practitioners

• Flexible, hybrid work environment

• 50% off home and car insurance

• Free RACV roadside care

• Discounts across RACV products and corporate partners

  
  

Our difference

At RACV, diversity and inclusion matter. We want our people to feel supported, valued and able to do their best work. If you need reasonable adjustments during the recruitment process or in the role, please let us know.

Application process

Learn more at careers.racv.com.au. Applicants must have the right to work in Australia and consent to a criminal record check. Submit your CV via the link.