AI Product Engineer

AI Product Engineer

Triskele Labs is a large-scale sovereign Australian cybersecurity provider. We deliver Managed Detection and Response (MDR), Digital Forensics and Incident Response (DFIR), offensive security, and Governance, Risk and Compliance (GRC) services to some of the most regulated and high-value organisations in Australia, across financial services, government, education, healthcare, and critical infrastructure.

Engineering excellence and operational discipline are core to how we win and keep clients. We invest in modern tooling, automation, and AI capability that allows a small, high-leverage engineering team to ship products and capability that would normally require a much larger headcount.

About the Role

The AI Product Engineer is a new and strategic role at Triskele Labs. This is the first time we have hired into this capability. You will report to the DevOps Architect and work alongside the AI DevOps Engineer who runs the AI platform you ship on top of.

This is an AI-native role. You will live inside frontier AI coding tools as your primary working environment. You are not expected to write code by hand. You are expected to design, scope, direct, review, integrate, and ship software at a pace and quality that would not be possible without AI as your engineering surface. We are hiring for taste, judgement, and outcome ownership, not for typing speed.

You will take on a portfolio of existing internal and client-facing products and continue to build them out. You will also be expected to identify, propose, and build new tools and products, both internal (operational leverage for our delivery teams) and external (client-facing or commercial).

This role demands strong product instincts, sharp engineering judgement, and the discipline to operate AI tooling at a professional standard. Triskele Labs is a fast-moving environment where individuals are trusted to make calls, ship, and own what they build.

Responsibilities

Product Engineering through AI

• Use frontier AI coding tools as your primary engineering environment to design, build, test, and ship software products.
• Operate these tools at a professional standard, including planning, decomposition, prompt and context engineering, evaluation, code review, and verification of every change before it ships.
• Own quality, security, performance, and maintainability of everything that goes to production, regardless of how it was authored.
• Stay at the leading edge of frontier AI coding tools and adjust working practices as the capability evolves.

Taking on Existing Products

• Inherit and extend a portfolio of existing internal and client-facing products built on modern PaaS, managed backend, and Git-based platforms.
• Work with the CTO and the DevOps team to understand current architecture, technical debt, and roadmap for each product.
• Improve reliability, security, observability, and user experience of inherited products without losing pace on new delivery.

Identifying and Building New Products

• Identify opportunities for new internal tools that create operational leverage for delivery, commercial, and corporate teams.
• Identify opportunities for new client-facing or commercial products that strengthen the Triskele Labs offering.
• Propose, scope, and prototype new products with the CTO and DevOps Architect, and take them through to production where the business case supports it.
• Drive products end to end: discovery, design, build, launch, iteration, and sunset where appropriate.

Working Across the Business

• Partner with the DevOps and AI platform engineering team for infrastructure, hosting, AI services, and shared platform capability.
• Engage directly with internal stakeholders across SOC, DFIR, GRC, Commercial, and Corporate to understand needs and validate solutions.
• Engage with clients where required to support, demonstrate, and improve client-facing products.

Engineering Discipline

• Apply strong software engineering fundamentals to everything you ship: clean architecture, sensible abstractions, sound data modelling, secure by default patterns, and pragmatic testing.
• Use modern Git-based source control workflows, code review practices, and CI/CD pipelines.
• Build with security, privacy, and data classification front of mind, given the regulated nature of our client base.
• Document architecture, decisions, and runbooks so products can be supported, extended, and handed over.