MLOps Engineer

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

About The Team:

BizAnalytics is a trusted internal brand, and a trusted team. We’re part of the wider Innovation and Growth team, and are jointly responsible for growth, and um.. innovation! Primarily, we’re about improving customer journeys at all stages of the insurance lifecycle, bringing a customer and product lens to BizCover’s decision making, to grow at scale in our Australian, and overseas, businesses.

We span the whole value chain, and a complete set of data skills, and drive value through insights, driving change, enabling better management decisions, and automating decisions with AI (and Machine Learning) live into our business

The Role:

We are looking for a talented MLOps Engineer to join our business insurance company and drive the operationalization of machine learning (ML) and AI Solutions. In this role, you will work closely with our internal teams, primarily our Data Scientist and Operational Excellence Manager, who leads our AI initiatives, to deploy, manage, and optimize ML-driven products that enhance our insurance offerings through customer support, compliance, digital, automated tools and many more. If you excel at bridging the gap between ML development and production-ready solutions, this is the role for you.

Requirements

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.
• Design, build, and maintain scalable ML pipelines for data processing, model training, validation, and deployment using modern frameworks and tools.
• Implement and manage continuous integration/continuous deployment (CI/CD) processes for ML systems to ensure reliability and rapid iteration of products.
• Monitor and optimize the performance, scalability, and stability of deployed ML models and GPT applications in production environments.
• Develop and maintain infrastructure for A/B testing ML models to validate improvements before full production deployment.
• 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.
• Automate model retraining, versioning, and evaluation processes to keep products aligned with evolving business needs and data trends.
• Troubleshoot and resolve issues related to system performance, data quality, or production failures, minimizing downtime and risk.
• Ensure all AI operations comply with data privacy, security, and regulatory standards relevant to the insurance industry.

Your Experience:

• 3+ years of experience in machine learning engineering or MLOps, with a focus on deploying and managing ML models in production.
• Proficiency in programming languages like Python and experience with MLOps tools (e.g., Kubeflow, MLflow, Airflow, or Docker).
• Proficiency in SQL and experience working with large datasets
• Hands-on experience building and maintaining ML pipelines, including data preprocessing, model deployment, and monitoring.
• Familiarity with CI/CD practices and cloud platforms (e.g., AWS, Azure, Google Cloud) for scaling ML solutions.
• Previous collaboration with data scientists or engineering teams to transition ML prototypes into production-ready systems.
• Knowledge of the insurance industry (e.g., risk modeling, claims processing) is a plus but not required.
• Strong problem-solving skills and the ability to thrive in a dynamic, fast-paced environment.
• Excellent communication skills to explain technical concepts to non-technical stakeholders.
• Bachelor’s degree in computer science, Engineering, Data Science, or a related field (Master’s preferred but not mandatory).

Benefits

• Hybrid working model with flexibility to work from home up to 3 days a week and a minimum of 2 days a week in the Sydney CBD office.
• Exciting and rewarding team culture
• Quarterly recognition awards
• Business Casual dress code
• Rewarding Employee Incentive Program
• Growing company with progression opportunities