Lewis Personnel Management
Senior Site Reliability Engineer (SRE) - AWS & Kurbernetes
- Compensation
- 150K–200K / mo
- Location
- Metro Manila
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Full time
- Experience
- 1+ years
- Category
- Sre
- Posted
Skills
Must have
- 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.
- AWS & Kubernetes: Advanced hands-on experience with AWS infrastructure and Kubernetes, particularly EKS.
- SRE/DevOps: Production reliability engineering, incident management, CI/CD, infrastructure as code, and automation.
- Programming & ML Infrastructure: Proficiency in at least one of Python, Ruby, Elixir, Go, JavaScript, or Rust, plus Python experience with ML-adjacent tooling.
Nice to have
- Startup experience.
- Experience deploying and managing ML workloads on Kubernetes.
- Experience with ML model serving, inference, or ML pipeline infrastructure.
- 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
Full description
Salary: ₱150,000 – ₱200,000 per month
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.