Job Description

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

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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.

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

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

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

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

The Role n

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.

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

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

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

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You’ll work with technologies such as:

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

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

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You’ll be involved in building and improving:

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- AI-powered workflows and automation
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- LLM-based applications and integrations
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- Data pipelines and processing systems
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- Automated QA and assessment tooling
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- Voice and conversational AI applications
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- Modern Databricks and Azure infrastructure
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- Internal tools and lightweight data products
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- Integrations between APIs, platforms and internal systems
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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 n

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- Build and ship AI-powered workflows, agents and automation
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- Integrate LLM APIs into real production workflows
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- Develop automation using tools such as n8n
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- Work with APIs, webhooks and event-driven pipelines
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- Prompt, evaluate and iterate LLM-based systems
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- Build RAG-style solutions and AI-powered applications
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- Integrate voice AI and other third-party AI services
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- Build and maintain data pipelines across multiple markets and data sources
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- Work with Databricks, Azure and modern cloud data platforms
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- Develop ingestion, transformation and data processing workflows
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- Work with structured and unstructured datasets
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- Build reliable, observable and maintainable data systems
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- Contribute to the evolution of the underlying data infrastructure
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Product & Building nn
- Build lightweight internal tools and dashboards
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- Integrate third-party platforms and services
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- Turn rough business requirements into working products
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- Prototype quickly and iterate based on what you learn
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- Take ownership of problems rather than waiting for someone else to tell you how to solve them
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What We’re Looking For n

This is a hands-on role .

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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.

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

The Kind of Person We’re Looking For: n

You build things because you’re curious.

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

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

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

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You have a bias towards action.

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

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You can work independently.

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

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You know when to ship.

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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.

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You have no ego.

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

Why This Role? n

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

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

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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