Senior 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.