AI Engineer

AI Engineer – Enterprise AI Enablement

-Sydney

-$160,000 - $180,000 + Super + Bonus

Join one of the most forward-thinking teams in Sydney, working within a business that is genuinely AI-first in its approach. There is a defined strategy, real budget, and enterprise-wide commitment to embedding AI across products and operations.

We’ve recently partnered closely with the business to appoint their Engineering Manager, and are now supporting the next phase of the build-out. Off the back of that hire, we’re adding multiple engineers to deliver on a pipeline of greenfield AI initiatives.

The focus is clear: strong software engineering foundations, with exposure to AI-native environments. This is not a data science role, it’s about engineers who can build the systems and platforms that enable agentic AI at scale.

You’ll be working on production-grade systems across multiple use cases, from customer-facing agents and chatbots to internal AI capability, partnering closely with teams responsible for fine-tuning models. A solid understanding of agentic ecosystems and LLMOps will go a long way in helping you succeed.

Overview of the Role

You’ll be responsible for building and owning scalable backend systems that power enterprise AI solutions. This is a hands-on engineering role, focused on writing high-quality code, automating workflows, and creating the foundations required for AI to operate reliably in production.

Operating in a modern DevOps environment, you’ll take end-to-end ownership across design, build, deployment, and ongoing optimisation. You’ll work closely with data and AI teams to integrate models into real-world applications, ensuring they are performant, scalable, and maintainable.

Key Skills

• 5+ years software engineering experience in Agile/DevOps environments, ideally with Python (OOP)
• Strong end-to-end SDLC experience across design, build, test, and production support
• Proven ability to build scalable backend systems and write production-grade code
• Experience with CI/CD pipelines and deployments in containerised environments (Docker/Kubernetes)
• Exposure to agentic AI / LLM frameworks (e.g. RAG, MCP, Vertex AI, LangChain, LlamaIndex)
• Understanding of LLMOps practices for deploying, monitoring, and scaling AI models

This is a strong opportunity for engineers who want to stay close to the tech, build meaningful systems, and play a key role in how AI is actually delivered across a large enterprise.