Overview

Required Qualifications:

  • Experience in administering cloud-based platforms (AWS, GCP, Azure)
  • Understanding the principles of building distributed and serverless systems
  • Knowledge of components and services of one or more cloud-based platforms (AWS, GCP, Azure)
  • Understanding the main principles of container-based platforms and tools
  • Experience working with/administering Kubernetes in its various flavours (e.g. on-prem, AWS EKS, Google K8s, Azure, OpenShift )
  • Experience in implementing infrastructure as code (e.g. Terraform, AWS CloudFormation, GCP Cloud Deployment Manager, Helm)
  • Experience with/theoretical understanding of building continuous delivery/continuous deployment
  • Knowledge of development methodologies, particularly with Agile/Scrum
  • Good spoken English

Nice to Have

  • Understanding of general ML/AI landscape and major areas of applications
  • Understanding of typical ML/AI software development lifecycle (e.g. experiment management, model training, hyperparameters optimisation, model serving)
  • Knowledge/theoretical understanding of typical ML tools and frameworks (e.g. Jupyter, TensorFlow, Keras, Pytorch, Katib, etc.)
  • Experience with one or more MLOps platforms (AWS Sagemaker, Google AI TFX, Kubeflow, MLFlow, Domino Labs, neu.ro etc.)
  • Experience with orchestration/configuration management tools (Ansible, Chef, etc.)
  • Main cloud providers’ certification (AWS, Azure, GCP)