Remote AWS Cloud Engineer for AI Training

ixolabs.ai; France; À distance; EUR 96 432 - 151 536; Plein temps

Résumé du poste

ixolabs.ai is seeking an AWS Cloud Engineer for AI training who can ensure AWS-based workloads are not only functional but well aligned with workload, cost, and security needs. This flexible role supports 10–25 hours/week and is remote, with immediate start and contractors paid weekly via Stripe or bank transfer.

You will review AI-generated code, design IAM and VPC architectures, and optimize costs while applying best practices across multiple AWS services.

Qualifications

• 5+ years building production systems on AWS.
• Deep familiarity with at least three AWS service families.
• Strong grasp of IAM, KMS, and AWS security best practices.
• Experience with CDK or Terraform for AWS infrastructure-as-code.
• Familiarity with multi-account, Control Tower, or Landing Zone architectures is a plus.

Responsabilités

• Generate and evaluate instruction-response pairs covering AWS compute, storage, networking, and serverless.
• Review AI-generated code in CDK, CloudFormation, Terraform, and Pulumi for AWS.
• Provide feedback on IAM design, least-privilege policies, and account architecture.
• Validate AI handling of VPC design, Transit Gateway, and PrivateLink.
• Evaluate cost-optimization recommendations (Compute Savings Plans, S3 lifecycle, RDS sizing).
• Identify subtle issues in IAM trust policies, KMS key access, and cross-account roles.

Connaissances

AWS production systems

IAM and security best practices

Terraform/CDK

IaC (CDK/ Terraform/ Pulumi)

Cost optimization awareness

Outils

CDK

Terraform

Pulumi

Description du poste

ixolabs.ai is seeking an AWS Cloud Engineer for AI training who can ensure AWS-based workloads are not only functional but well aligned with workload, cost, and security needs. This flexible role supports 10–25 hours/week and is remote, with immediate start and contractors paid weekly via Stripe or bank transfer.

You will review AI-generated code, design IAM and VPC architectures, and optimize costs while applying best practices across multiple AWS services.