Chubb is a world leader in insurance. With operations in 54 countries and territories, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance and life insurance to a diverse group of clients. As an underwriting company, we assess, assume and manage risk with insight and discipline. We service and pay our claims fairly and promptly. The company is also defined by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength and local operations globally. Parent company Chubb Limited is listed on the New York Stock Exchange (NYSE: CB) and is a component of the S&P 500 index. Chubb maintains executive offices in Zurich, New York, London, Paris and other locations, and employs approximately 40,000 people worldwide. Additional information can be found at: www.chubb.com.
Chubb celebrates diversity by fostering an inclusive, flexible and equitable workplace. We support applications from all members of our community and equitable access to our employment opportunities. We are open to discussing workplace flexibility in all our vacancies, to ensure we can attract the best candidates and accommodate individual needs, differences, disabilities and working arrangements. Please let us know if you require any adjustments to the recruitment process so we can support you to present your best self.
Chubb is looking for an AI Engineer to help drive the next wave of analytics and AI capability through the CI Transformation program in the Australia Middle Market Team.
As an AI Engineer, you’ll work closely with underwriting, operations, actuarial, and business teams to understand needs, shape requirements, and help define what great solutions look like. You’ll also partner with global and offshore teams to bring those solutions to life and ensure they deliver measurable business value.
In this role, you will:
- Support the delivery of AI and machine learning solutions that improve underwriting, operations, and decision-making
- Analyse business requirements and help inform solution design, including the evaluation and integration of external data sources
- Apply modern AI techniques such as LLMs, RAG, prompt engineering, embeddings, and agentic workflows alongside traditional data science methods
- Work with MLOps and data engineering teams to support production deployment, monitoring, and ongoing model quality
- Contribute to end-to-end delivery, from problem framing and exploration through to validation, deployment, and adoption
- Present findings, model performance, and progress to business stakeholders in clear, practical terms
- Maintain strong documentation across the solution lifecycle
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