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Cloud Infrastructure and DevOps Engineer
2 months ago
Groupe SII is seeking a highly skilled Cloud Native Software Engineer to join our team. As an AWS/K8S Engineer, you will be responsible for designing, developing, and maintaining cloud-native architecture solutions for our applications.
Key Responsibilities- Product Development: Develop and maintain products that will be used by our Data Science community, ensuring they meet the required standards and are scalable.
- Cloud Architecture: Design and implement cloud-native architecture solutions for our application needs, particularly with AWS, ensuring they are secure, efficient, and cost-effective.
- Infrastructure-as-Code: Utilize Terraform and AWS Cloud Formation to provide infrastructure-as-code, ensuring consistency and reproducibility across our environments.
- Platform Support: Provide support for our platform, ensuring it is running smoothly and efficiently, and addressing any issues that may arise.
- Cloud Infrastructure: Provision and maintain cloud infrastructure that will support training machine learning models, ensuring it meets the required standards and is scalable.
- Technical Expertise: Wide experience with Kubernetes and Docker is a must-have, as well as industry experience with Amazon Web Services, IAM, VPC, API Gateway, NLB, ALB, EC2, ECS, EKS, Lambda, S3, RDS, DynamoDB, SQS, etc.
- Programming Skills: Experience in Python and Bash scripting, as well as knowledge of Linux systems.
- CI/CD/CT Pipelines: Experience in implementing CI/CD/CT pipelines.
- Networking: Good understanding of networking and related protocols (DNS, TLS, TCP).
- Troubleshooting: Demonstrated experience in troubleshooting problems and working with a team to resolve production issues.
- Cloud Provisioning Tools: Understanding of cloud provisioning tools, e.g. CloudFormation and Terraform.
- Database Technologies: Good understanding of database technologies.
- DevOps Toolkit: Familiarity with the DevOps toolkit (Terraform, Ansible, Chef, and other tools).
- Messaging Pub/Sub Systems: Exposure to messaging pub/sub systems (e.g. AWS SNS, SQS, RedisQ etc.).
- Data Science IDEs: Exposure to data science IDEs like Rstudio, Jupyter notebook, and SAS studio is a huge plus.
- Machine Learning Workflow: Understanding of Machine Learning workflow and apps like Kubeflow and Sagemaker studio is a huge plus.
