Senior Cloud Platform Engineer with AI Enablement

We are looking for a Senior Cloud Platform Engineer with AI Enablement experience for a project for our client from the EdTech industry.

• Senior-level experience as a Cloud, DevOps, Platform, SRE or Infrastructure Engineer.
• Advanced, hands-on AWS experience including core services (EC2, VPC, IAM, S3, RDS, EKS, Lambda, CloudWatch) and a track record of running production workloads on AWS.
• Kubernetes in production or near-production (EKS preferred).
• Strong Infrastructure as Code skills, especially Terraform.
• Familiarity with CI/CD tools (GitHub Actions, GitLab CI/CD, Jenkins, Azure DevOps or Tekton).
• Experience with observability and monitoring tools (Prometheus, Grafana, ELK, Loki, Datadog, New Relic, CloudWatch or OpenSearch).
• Production support, incident management, RCA and deployment stability.
• Good scripting or programming skills (Python, Bash, PowerShell or Go).
• Security fundamentals, IAM, secrets management and secure cloud delivery.
• Experience working with development teams and improving Developer Experience.
• Ability to create standards, documentation, automation and reusable platform components.

Nice to have:

• Experience with Azure or GCP as a secondary cloud.
• AWS certifications (Solutions Architect Professional, DevOps Engineer Professional, or similar).
• Practical experience or strong interest in using AI in engineering work, including AI tools for cloud operations, AI-assisted log analysis, script generation, documentation, coding assistants, LLM tools for monitoring, and creating guidelines for controlled AI usage.

• Design and develop cloud-native platforms and internal developer platforms (IDP) on AWS.
• Deliver scalable, reliable and secure platform solutions for engineering teams.
• Work hands-on with Kubernetes (EKS) and a broad range of AWS services.
• Build and maintain Infrastructure as Code (Terraform, Pulumi, CloudFormation/CDK).
• Develop CI/CD pipelines and automate deployments.
• Introduce platform standards, reusable templates, golden paths and best practices.
• Improve observability, monitoring, alerting and incident response.
• Support reliability, high availability, disaster recovery and operational stability.
• Use AI tools and AI-assisted workflows to boost engineering productivity and platform operations.
• Help define safe, controlled usage of AI tools, coding agents and LLM-based workflows.
• Support teams with AI-assisted documentation, runbooks, log analysis, script generation, CI/CD and infrastructure review.
• Ensure AI usage is secure, observable and aligned with engineering standards.