AI Engineer / GenAI Specialist – Type: Full-time / Permanent
Industry: Insurance, FinTech, AI/ML
Reports to: Head of Data & AI / AI Product Lead
About We power more than 25 leading brands including IAG, Woolworths, Medibank, Bupa, HCF, Petbarn, AA (NZ) and more.
Role Overview We are looking for a
hands-on AI Engineer / GenAI Specialist who thinks like a
product person. Your mission is to design, build, and deploy AI solutions (LLMs, GenAI, ML models) that solve real business problems across claims, customer experience, fraud detection, underwriting, and internal operations.
You will work closely with product, technology, data, operations and commercial teams to turn AI ideas into production-ready solutions — focusing on practical application, business impact, and responsible AI practices.
Key Responsibilities AI / GenAI Product Development- Design and build AI-powered features using LLMs, RAG pipelines, embeddings, vector databases and retrieval systems.
- Develop prototypes and production solutions solving real problems such as claims automation, digital assistants, summarisation, workflow optimisation, and customer insights.
- Translate business requirements into technical solutions that balance value, feasibility, cost and risk.
Technical Delivery- Build scalable AI services and APIs using modern engineering practices.
- Implement prompt engineering, fine-tuning, evaluation, guardrailing, and monitoring.
- Own end-to-end AI lifecycle: data preparation → model selection → deployment → monitoring.
Business Problem Solving- Work with stakeholders to deeply understand the insurance and pet health domain.
- Identify where AI should and should not be used.
- Estimate ROI, effort, and impact of proposed AI initiatives.
- Reframe ambiguous or complex problems into structured AI opportunities.
Collaboration & Communication- Partner with product managers, designers, data engineers, operations and legal.
- Present AI concepts and solutions to non-technical stakeholders clearly.
- Help uplift AI literacy across the organisation.
Ethical & Responsible AI- Mitigate model risks such as hallucinations, bias, privacy and explainability.
- Ensure compliance with insurance and regulatory constraints.
- Build safe and trustworthy AI systems.