BizCover who? You haven’t heard of us?
We dominate the SME business insurance market by having an online platform that makes comparing and buying business insurance a super easy process. Not to toot our own horn but we have been recognized in Deloittes fast 50 companies as one of the fastest growing technology companies and Westpac’s top 20 businesses of tomorrow - #killingit

YOUR EXPERIENCE:

• 1-2 years of experience in AI and MLOps, with a focus on deploying and managing AI/ML models in production.
• Proficiency in programming languages like Python and experience with MLOps tools (e.g., Kubeflow, MLflow, Airflow, or Docker).
• Familiarity with CI/CD practices and cloud platforms (e.g., AWS, Azure, Google Cloud) for scaling ML solutions.
• Experience with Multi-agent frameworks involving Voice and conversational AI interfaces is advantageous but not mandatory.
• Keep current with emerging AI and LLM developments and suggest enhancements or innovative solutions when relevant
• Bachelor’s degree in computer science, Engineering, Data Science, or a related field

THE ROLE:

We are looking for a talented ML/AI Engineer to join our business insurance company. This role sits at the forefront of a high-growth technology initiative driving the operationalization of machine learning (ML) and AI Solutions. You will be part of the data science team inside Analytics, working alongside data scientists, operations excellence manager, and collaborating across Operations, Risk, TechOps and Cyber to engineer robust, resilient AI and ML solutions.

ROLES AND RESPONSIBILITIES:

• Partner with the Data Scientist to operationalize ML models and build AI-powered processes, ensuring seamless deployment of business products like risk assessment tools, claims automation, and customer-facing insurance solutions.
• Implement and manage continuous integration/continuous deployment (CI/CD) processes for AI/ML systems to ensure reliability and rapid iteration of products.
• Hands-on experience with LLM deployment and integration of foundation models into business applications.
• Collaborate with cross-functional teams to integrate AI products into existing business systems, ensuring compatibility and efficiency.
• Fine-tune and evaluate AI/ML models based on real-world business requirements and feedback.