Description:
We value innovation and ongoing improvement, so you’ll be encouraged to keep up with the latest practices in MLOps and in the pet insurance industry. You’ll also have the opportunity to test and introduce new AI models, technologies, and frameworks to keep our data and modelling practices up to date and effective.
Your responsibilities
- Design, build, and deploy AI / Machine Learning systems in production to solve business problems.
- Translate problem statements into scalable AI/ML solutions, focusing on model implementation, performance, and reliability.
- Own the end-to-end engineering of AI/ML pipelines, from data ingestion through deployment and monitoring.
- Contribute to shaping and evolving our MLOps strategy, including model monitoring, retraining pipelines, and best practices for versioning and deployment.
- Evaluate and implement new tools and frameworks to improve our end-to-end AI/ML lifecycle, from experimentation to production.
- Collaborate with product managers, engineers, and data engineers to integrate models and ensure robust data pipelines and infrastructure.
- Understanding advanced statistical analysis, machine learning, and data mining to identify patterns and generate actionable insights.
- Communicate complex models and findings to stakeholders through visualisations, reports, and presentations.
- Stay updated on emerging trends in data science, ML/AI, and the pet insurance industry; implement new tools and frameworks to enhance workflows.
- Participate in Agile or Kanban methodologies, contributing to a collaborative, flexible team environment.
- Maintain strong awareness of data privacy and security requirements, ensuring compliance with relevant regulations.
Essential
Your skills and experience
- Hands-on experience deploying and managing machine learning workflows on Google Cloud Platform, particularly using Vertex AI (model training, endpoint deployment, and monitoring).
- Experience architecting and maintaining CI / CD pipelines that deliver models into production.
- Comfortable working with cloud infrastructure and Infrastructure as Code (IaC), ideally with Terraform, to support scalable ML systems.
- Strong understanding of data governance, data lineage, and security practices.
- Ability to communicate effectively with both technical and non-technical audiences.
- Enthusiasm for working in an Agile/Kanban setup within a fast-paced, scale-up environment.