About the job
About the Role
Join our dynamic Insurance AI team as a Machine Learning Engineer, where your expertise will serve as the engineering backbone for our Data Scientists. You will be responsible for building and maintaining the machine learning infrastructure that transforms models into reliable and scalable products.
This role is not about starting from scratch. Our Sydney-based AI & Computer Vision team has already established robust ML tools and pipelines. Your mission is to extend, adapt, and maintain that infrastructure for specific use cases within the US market. If you find fulfillment in optimizing existing systems rather than reinventing them, this position is for you.
You will collaborate closely with Data Scientists in the US and ML Engineers in Australia, acting as a key technical liaison to keep both teams operating efficiently.
What You'll Do
You will lead the ML engineering function for the US Insurance AI team, which involves building data and model pipelines, integrating internal and external APIs, and ensuring our Data Scientists have the necessary tools to deploy models into production. Daily collaboration with our Sydney AICV team will allow you to leverage shared infrastructure and contribute improvements.
Your daily tasks will include writing Python code, managing data pipelines, troubleshooting production issues, and converting Data Scientist requirements into functional systems. You will utilize AWS and engage with cloud-native technologies while working within an established MLOps framework.
Key Responsibilities
- Develop and maintain ML pipelines for data ingestion, feature processing, model training, deployment, and monitoring on AWS.
- Adapt existing tools from our Sydney AICV team to meet US Insurance AI needs.
- Create and support internal tools and frameworks that enhance experimentation and speed up delivery.
- Integrate various internal and external APIs to connect datasets, models, and services.
- Collaborate with Data Scientists to understand their workflow requirements and translate them into scalable technical solutions.
- Ensure that the infrastructure supports rapid experimentation while ensuring reliability, security, and scalability.
- Work with Technical Product Managers, API engineers, and platform teams to deploy models in a production environment.
- Contribute to the shared codebase via feature branches, pull requests, and code reviews.
