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)
Solid
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
.
#J-*****-Ljbffr

📌 Ai Engineer (New South Wales)
🏢 Preacta
📍 New South Wales