AWS Cloud DevOps Engineer (Only W2)
Iris Software Inc.
An application made for this job — a tailored resume and cover letter that speak straight to the posting.
Job summary
Iris Software Inc. seeks an AWS Cloud DevOps Engineer for our Fortune 100 direct client in Berkeley Heights, NJ. This hybrid role requires 3 days onsite weekly to design and implement cloud infrastructure supporting AI/ML workloads.
You will build scalable environments with SageMaker, automate provisioning with CloudFormation and Terraform, and collaborate with data science and DevOps teams to optimize cost, performance, and reliability in AWS.
Qualifications
- Experience provisioning AWS infrastructure for AI/ML workloads.
- Background in Cloud DevOps with CDK and Terraform.
- Experience with SageMaker model training and deployment.
Responsibilities
- Design, develop, and implement cloud infrastructure solutions on AWS with a focus on AI/ML workloads.
- Build and manage scalable environments using Amazon SageMaker for model training, deployment, and lifecycle management.
- Automate infrastructure provisioning using IaC tools (e.g., CloudFormation, Terraform) adhering to security and compliance standards.
- Collaborate with data science and DevOps teams to optimize performance, cost, and scalability of AI/ML pipelines in the cloud.
Skills
Tools
Job description
Iris's Fortune 100 direct client is looking AWS Cloud DevOps Engineer.
Toronto: Berkeley Heights, NJ (Hybrid, 3 days onsite in a week)
AWS cloud developer for infrastructure provisioning skills and Cloud DevOps background and worked/working experience on CDK/TerraForm
- Design, develop, and implement cloud infrastructure solutions on AWS with a focus on AI/ML workloads.
- Build and manage scalable environments using Amazon SageMaker for model training, deployment, and lifecycle management.
- Automate infrastructure provisioning using IaC tools (e.g., CloudFormation, Terraform) adhering to security and compliance standards.
- Collaborate with data science and DevOps teams to optimize performance, cost, and scalability of AI/ML pipelines in the cloud.
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