Build AI systems. Automate the boring stuff. Work with interesting data. Ship things people actually use.

A growing, data-driven organisation is expanding its Data & AI capability and is looking for a hands-on Data & AI Builder to join the team.

This is a role for someone who enjoys figuring things out, building quickly and turning ideas into working production systems.

It’s not a traditional data science role. You won’t be spending your days training custom ML models.

It’s also not an architecture role where you spend your time drawing diagrams.

We’re looking for someone who can write the code, build the workflow, integrate the APIs and ship the solution.

The Role

The role sits roughly 50/50 between AI/LLM engineering and data engineering, with the balance shifting depending on what’s happening across the business.

On the data side, you’ll work across modern cloud data infrastructure, pipelines and large-scale datasets.

On the AI side, the focus is predominantly on automation and integration rather than building models from scratch.

Think roughly 70% automation/integration and 30% model-focused work.

You’ll work with technologies such as:

- LLM APIs including Claude, Gemini and VAPI
- n8n and other workflow automation tools
- Databricks and Azure
- Python and SQL
- APIs, webhooks and event-driven workflows
- RAG and prompt engineering
- LLM evaluation

One day you could be building a Databricks notebook. The next, you might be debugging an n8n webhook or integrating an LLM into a production workflow.

What You’ll Be Working On

There are multiple AI and data initiatives underway, giving you the opportunity to work across a broad range of problems.

You’ll be involved in building and improving:

- AI-powered workflows and automation
- LLM-based applications and integrations
- Data pipelines and processing systems
- Automated QA and assessment tooling
- Voice and conversational AI applications
- Modern Databricks and Azure infrastructure




- Internal tools and lightweight data products
- Integrations between APIs, platforms and internal systems

There’s plenty of opportunity to move from early-stage POC through to production, rather than simply handing ideas over to another team.

What You’ll Actually Do

GenAI & Automation

- Build and ship AI-powered workflows, agents and automation
- Integrate LLM APIs into real production workflows
- Develop automation using tools such as n8n
- Work with APIs, webhooks and event-driven pipelines
- Prompt, evaluate and iterate LLM-based systems
- Build RAG-style solutions and AI-powered applications
- Integrate voice AI and other third-party AI services
- Build and maintain data pipelines across multiple markets and data sources
- Work with Databricks, Azure and modern cloud data platforms
- Develop ingestion, transformation and data processing workflows
- Work with structured and unstructured datasets
- Build reliable, observable and maintainable data systems
- Contribute to the evolution of the underlying data infrastructure

Product & Building

- Build lightweight internal tools and dashboards
- Integrate third-party platforms and services
- Turn rough business requirements into working products
- Prototype quickly and iterate based on what you learn
- Take ownership of problems rather than waiting for someone else to tell you how to solve them

What We’re Looking For

This is a hands-on role.

You’ll get plenty of technical direction from senior engineers and team leads,



so we’re not looking for someone who wants to spend their time defining enterprise architecture.

We’re looking for someone who can independently take a problem and build something useful.

The Kind of Person We’re Looking For:

You build things because you’re curious.

You’ve got side projects, experiments, prototypes or tools you’ve built because you thought “surely this could be automated.”

You use AI in your day-to-day work.

You’re already experimenting with AI-native tooling and looking for ways to make yourself and your team more productive.

You have a bias towards action.

You don’t need a perfect requirements document before you start. You can assess a problem, make a sensible call and start building.

You can work independently.

You’ll have technical support around you, but you won’t need to be managed closely.

You know when to ship.

You understand the difference between something that needs more engineering and something that’s good enough to get into the hands of users and learn from.

You have no ego.

Titles aren’t important. Positive ideas are. You should be comfortable challenging ideas, receiving feedback and changing your mind when someone has a better approach.

Why This Role?

- Work on real production AI, not just experiments and demos
- Work with LLMs, voice AI and automation
- Get hands-on with Databricks and modern data infrastructure
- Work across AI, data engineering, automation and product
- Have genuine ownership from day one
- Build solutions that have a direct business impact
- Work in a fast-moving environment where curiosity and getting things shipped are valued

Show Us What You’ve Built

We care much more about what you can actually build than what your CV says.

If you’ve got a GitHub repo, working demo, side project, Loom walkthrough or something you’ve built because you couldn’t stop thinking about the problem — we’d love to see it.

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📌 Artificial Intelligence Engineer (Sydney)
🏢 Talent Insights Group
📍 Sydney