Site Reliability Engineer (Machine Learning) - REMOTE

Location: Metro Manila, Philippines | Remote Employment type: Full-time | Day Shift About the role This is a fully remote Site Reliability Engineer (SRE) position with Machine Learning Expertise, partnering with Japan's leading restaurant reservation management platform. You will maintain and scale a 24/7 high-traffic production environment built on AWS and Kubernetes, while bringing SRE and MLOps discipline to machine learning pipelines and inference services. Key responsibilities Maintain and scale a 24/7 high-traffic production environment built on AWS and Kubernetes Design, build, and evolve Infrastructure as Code (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 About you Minimum 2 years of hands-on experience with AWS, with significant depth in AWS 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 Job Ref: RYAN - TT