Remote SRE & MLOps Engineer | AWS & Kubernetes

Lewis Personnel Management

Metro Manila

Remote

PHP 1,200,000 - 1,700,000

Full time

2 days ago

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Job summary

Lewis Personnel Management is seeking an experienced Site Reliability Engineer (SRE) with ML expertise for a fully remote role in Metro Manila, Philippines. You will maintain and scale a 24/7 production environment on AWS and Kubernetes for a Japan-based restaurant platform.

Responsibilities include designing IaC with Terraform, Helm, and ArgoCD; managing AWS services including EKS, EC2, RDS, and S3; driving incident response and observability; and enabling ML model deployment pipelines on

Qualifications

Minimum 2 years of hands-on AWS experience with EKS, EC2, RDS, Fargate, CloudFront, Lambda, and S3.
Deep experience deploying, managing, and scaling production workloads on Kubernetes.
Strong familiarity with MLOps practices and hands-on Python experience with ML-adjacent tooling (e.g., inference serving, model deployment, ML pipelines).
Experience in direct software engineering under DevOps/SRE practices, including at least 1 year in a Technical Lead capacity.
Current proficiency in Python (preferred) or Ruby, Elixir, Go, JavaScript, or Rust.
Strong skills in configuration management (YAML, Bash) along with containerization and hypervisor fundamentals.
Track record of operating large-scale production systems with a deep understanding of fault-tolerance and distributed systems failure modes.
Experience in fast-paced startup environments.
Infrastructure as Code & GitOps tools: Terraform, Pulumi, ArgoCD
Observability stacks: Prometheus, Grafana

Responsibilities

Maintain and scale a 24/7 production environment on AWS and Kubernetes.
Design, build, and evolve IaC using Terraform, Helm, and ArgoCD.
Manage AWS core services including EKS, EC2, RDS, Fargate, CloudFront, Lambda, and S3.
Drive incident response, proactive monitoring, configuration management, and postmortem processes.
Implement DevOps best practices to boost developer productivity, platform scalability, and system reliability.
Apply SRE discipline to ML infrastructure, ensuring high availability, observability, and performance for model training and inference pipelines.
Support, streamline, and automate ML model deployment pipelines on Kubernetes.
Build monitoring, logging, and alerting systems tailored to production ML models.
Partner with data science and engineering teams to operationalize and scale intelligent features across the platform.