Remote SRE: AWS & Kubernetes, ML Infra Focus

Lewis Personnel Management; Metro Manila; Remote; PHP 900,000 - 1,500,000; Full time

Job summary

Lewis Personnel Management seeks a Site Reliability Engineer (SRE) to maintain and evolve production infrastructure across AWS and Kubernetes. The role emphasizes reliability, scalability, automation, and incident management, with responsibilities for ML infrastructure.

You will operate a 24/7 Kubernetes-based production stack, lead incident responses, implement IaC with Terraform, Helm, and ArgoCD, and collaborate with data scientists to operationalize ML models at scale.

Qualifications

• 2+ years of AWS experience with production use.
• Hands-on experience operating AWS EKS in production.
• Software engineering experience applying DevOps/SRE practices, including 1+ year in a technical leadership capacity.
• Experience operating production systems at scale with incident response.
• Experience with ML workflows, MLOps, or ML infrastructure.

Responsibilities

• Maintain and improve a 24/7 production environment on Kubernetes.
• Apply SRE and DevOps practices to improve reliability and automation.
• Monitor systems, manage configuration, performance, and scalability.
• Lead incident response, troubleshooting, and postmortems.
• Manage AWS infrastructure including EKS, EC2, RDS, Fargate, CloudFront, Lambda, and S3.
• Build CI/CD pipelines and infrastructure as code using Terraform, Helm, and ArgoCD.
• Identify reliability risks and implement scalable solutions.

Skills

AWS & Kubernetes

SRE/DevOps

Programming & ML Infra

Tools

Terraform

Helm

ArgoCD

AWS EKS

Job description

Lewis Personnel Management seeks a Site Reliability Engineer (SRE) to maintain and evolve production infrastructure across AWS and Kubernetes. The role emphasizes reliability, scalability, automation, and incident management, with responsibilities for ML infrastructure.

You will operate a 24/7 Kubernetes-based production stack, lead incident responses, implement IaC with Terraform, Helm, and ArgoCD, and collaborate with data scientists to operationalize ML models at scale.