This role asks for an engineer who can take hard product problems from first idea through to production, shaping the user experience, capabilities and data models of an AI support product. A strong candidate has a few years of shipping code in a demanding engineering setting, ideally at a startup or scaleup, and brings the judgment to decide when AI generated work is valuable enough to release. Comfort with coding agents as a real part of the build workflow matters more than any single tool.

How it fits the AI era

The work sits at the center of AI product development, where the shape of the interface, the data models and the feedback loops are still being figured out. AI takes on much of the drafting and code generation, so the human contribution shifts toward framing problems, building the tooling that keeps agents effective, and judging output quality before it reaches customers. In a regulated setting, that judgment is the part that cannot be handed off.

How it builds AI experience





Someone here would gain hands on practice running coding agents as a core workflow and building the feedback systems that make them reliable, which is directly relevant to a company selling AI support agents into regulated industries. The work trial and daily use of tools like Claude Code offer a concrete way to build that experience. A candidate can prepare by showing how they have used agents on real shipped work and how they checked the results.

Worth showing

- Shipping code to production
- Coding agent workflows
- Startup or scaleup experience
- Engineering judgment on output
- Building feedback tooling

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Lorikeet

AI customer-support agents built for the regulated industries where a wrong answer actually matters.

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📌 Software Engineer (Sydney)
🏢 present of AI
📍 Sydney