Working in a fast developing engineering team, the position entails contributing in the creation and continual enhancement of the ML release pipeline and infrastructure. You will collaborate closely with the data science and engineering teams to install and manage machine learning workloads in a scalable and effective way. You will assist the team's emphasis on ML Ops awareness in terms of deployment methods as well as operational monitoring and alerts.
The Role:
1. Aid in the development and implementation of machine learning systems
2. Develop and deploy scalable tools and services for the data science team to run machine learning workloads for training and predictions
3. Create data pipelines and technical infrastructure to help the data science team grow workloads
4. Work with Data Science and DevOps teams to productionise and release models following change processes and best practice
Requirements:
5. Hands on experience with any of the following Terraform, AWS/Azure, Docker, Kubernetes, Jenkins, CI/CD
6. Scripting experience in Python and Bash
7. Cloud experience (AWS, Azure or GCP)
8. Hands on experience with Linux and Windows systems