Artificial Intelligence Engineer

Sydney | Hybrid | Full-Time Data & AI Engineer 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 VAPIn8n and other workflow automation toolsDatabricks and AzurePython and SQLAPIs, webhooks and event-driven workflowsRAG and prompt engineeringLLM evaluationModern cloud data platforms 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 automationLLM-based applications and integrationsData pipelines and processing systemsAutomated QA and assessment toolingVoice and conversational AI applicationsModern Databricks and Azure infrastructureInternal tools and lightweight data productsIntegrations 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 automationIntegrate LLM APIs into real production workflowsDevelop automation using tools such as n8nWork with APIs, webhooks and event-driven pipelinesPrompt, evaluate and iterate LLM-based systemsBuild RAG-style solutions and AI-powered applicationsIntegrate voice AI and other third-party AI services Data Engineering Build and maintain data pipelines across multiple markets and data sourcesWork with Databricks, Azure and modern cloud data platformsDevelop ingestion, transformation and data processing workflowsWork with structured and unstructured datasetsBuild reliable, observable and maintainable data systemsContribute to the evolution of the underlying data infrastructure Product & Building Build lightweight internal tools and dashboardsIntegrate third-party platforms and servicesTurn rough business requirements into working productsPrototype quickly and iterate based on what you learnTake 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. You’ll ideally have: Strong Python and SQL skillsCommercial experience across data engineering and/or software engineeringExperience working with modern cloud data platformsExperience building data pipelines and integrationsExposure to LLMs and GenAI applicationsExperience working with APIs and webhooksA good understanding of workflow automationExperience with tools such as Databricks, Azure, n8n or similarAn understanding of prompt engineering, RAG or LLM evaluationEnough frontend/product capability to build a functional internal tool when required You don’t need to tick every technology box. We’re much more interested in how you think, what you’ve built and how quickly you can learn. 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. Good 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 demosWork with LLMs, voice AI and automationGet hands-on with Databricks and modern data infrastructureWork across AI, data engineering, automation and productHave genuine ownership from day oneBuild solutions that have a direct business impactWork 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. Show us, don’t just tell us.