Site Reliability Engineer (SRE) - AWS & Kurbernetes

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

Location: Remote — Dayshift
Employment Type: Full-Time

Job Summary

We are seeking a Site Reliability Engineer (SRE) to maintain and evolve production infrastructure across AWS and Kubernetes. The role is primarily focused on SRE, reliability, scalability, automation, and incident management, with additional responsibility for applying SRE practices to machine learning infrastructure, model deployment, and MLOps workflows.

Key Responsibilities

SRE & Cloud Infrastructure

• Maintain and improve a 24/7 production environment running on Kubernetes.

• Apply SRE and DevOps practices to improve reliability, automation, and engineering efficiency.

• Monitor systems proactively and manage configuration, performance, and scalability.

• Lead incident response, troubleshooting, root-cause analysis, and postmortems.

• Manage and evolve AWS infrastructure including EKS, EC2, RDS, Fargate, CloudFront, Lambda, and S3.

• Build and maintain CI/CD pipelines and infrastructure as code using Terraform, Helm, and ArgoCD.

• Identify reliability risks and implement solutions for large-scale production systems.

Machine Learning Infrastructure

• Apply SRE principles to ML infrastructure, including model serving, training pipelines, and data systems.

• Support ML model deployment pipelines and MLOps practices.

• Monitor ML model performance and implement observability and alerting for ML systems.

• Collaborate with data scientists and product teams to operationalize ML models at scale.

• Support ML workloads running on Kubernetes and AWS.

Required Experience

• 2+ years of AWS experience, including production use of EKS, EC2, RDS, Fargate, CloudFront, Lambda, and S3.

• 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, including incident response and troubleshooting.

• Experience with machine learning workflows, MLOps, or ML infrastructure.

Nice-to-Have Experience

• Startup experience.

• Experience deploying and managing ML workloads on Kubernetes.

• Experience with ML model serving, inference, or ML pipeline infrastructure.

Required Skills

1. AWS & Kubernetes: Advanced hands-on experience with AWS infrastructure and Kubernetes, particularly EKS.

2. SRE/DevOps: Production reliability engineering, incident management, CI/CD, infrastructure as code, and automation.

3. Programming & ML Infrastructure: Proficiency in at least one of Python, Ruby, Elixir, Go, JavaScript, or Rust, plus Python experience with ML-adjacent tooling.

Preferred Skills

• Terraform or Pulumi

• Helm and ArgoCD

• Prometheus and Grafana

• YAML and Bash

• Container and hypervisor fundamentals

• PostgreSQL and MongoDB

• Kafka and event-driven architecture

• Security, PCI-DSS, GDPR, and digital forensics

• TensorFlow Serving, TorchServe, or Triton

• Feature stores, experiment tracking, and model registry tools

• ML model deployment and inference serving

• MLOps and ML observability