Head of AI Product Engineering

About the role

MYOB, a business management software company serving Australia and New Zealand, is looking for a senior technology leader to lead the engineering of its customer-facing AI products and turn its AI strategy into scalable customer experiences. You will lead a multidisciplinary team spanning AI engineering, data science and software engineering, working closely with Product and Design to take agentic AI products from idea to production, and set the technical direction for how AI is built at MYOB.

Key responsibilities

• Own the engineering of MYOB's agentic AI products from discovery through to production and ongoing operation.
• Define the target technical state of the AI products with the GM, Architecture and engineering teams, guiding architecture, platform and investment decisions.
• Set evaluation frameworks, quality measures and release criteria, balancing accuracy, latency, reliability, safety and cost.
• Define how data is sourced, curated and improved, using existing data, redesigned product experiences and customer feedback loops.
• Embed responsible AI in architecture and product design, including human oversight, informed consent, auditability, reversibility, privacy and security.
• Lead and coach senior technical leaders across AI engineering, data science and software engineering, partnering with Product, Design, Security, Architecture and external AI partners.

Required profile

• Built and operated ML, LLM or AI-powered products in production, with strong software engineering and architecture capability.
• Led senior technical leaders and multidisciplinary engineering teams, creating clarity, accountability and strong technical cultures.
• Comfortable partnering with Product and Design, shaping technical strategy and getting into engineering detail; experience in responsible AI or regulated environments highly regarded.

Required skills

• Understanding of agentic systems and LLM architecture, and the trade-offs between model quality, latency, cost, reliability and safety.
• Use of evaluation frameworks, test datasets, experimentation and production measurement to judge readiness to ship and continuously improve AI products.
• Understanding of data sourcing, curation and enhancement, including signals created through product experiences.