Site Reliability Engineer – ML Infrastructure

TableCheck

Site Reliability Engineer maintaining AWS and Kubernetes infrastructure for TableCheck, a restaurant reservation platform. Focusing on SRE duties and ML initiatives for reliable system operations.

Posted 7/14/2026full-timeRemote • 🇯🇵 JapanJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in maintaining production environments using Kubernetes and AWS, with a strong focus on implementing DevOps methodologies and MLOps practices. Proficient in building CI/CD pipelines and ensuring system reliability and performance across ML infrastructure.

Highest-signal resume keywords

AWS EKSKubernetes ManagementCI/CD Pipeline DevelopmentDevOps MethodologiesMLOps Practices

ATS Keywords

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Hard Skills

PythonRubyElixirGoJavascriptRustConfiguration ManagementTerraformHelmYAML

Tools & Technologies

AWS EC2AWS RDSAWS FargateAWS CloudFrontAWS LambdaAWS S3Monitoring ToolsIncident ResponsePostmortem ProcessesInfrastructure as Code

Industry Keywords

SRE PrinciplesProduction EnvironmentSystem ReliabilityML InfrastructureModel DeploymentData SystemsObservabilityMachine Learning Workflows

Tech Stack

Tools & technologies

AWSEC2ElixirGoJavaScriptKubernetesPythonRubyRustTerraform

About the role

Key responsibilities & impact

Following SRE principles to maintain a 24/7 production environment running on Kubernetes
Implementation of DevOps methodologies to improve IT team quality of life
Proactive system monitoring and configuration
Incident response and postmortem processes
Managing and evolving AWS infrastructure (EKS, EC2, RDS, Fargate, CloudFront, Lambda, S3)
Building and maintaining CI/CD pipelines, infrastructure as code (Terraform, Helm, ArgoCD)
Ensuring system reliability, performance, and scalability across our production stack
Applying SRE discipline to ML infrastructure — ensuring model serving, training pipelines, and data systems are reliable, observable, and well-operated
Supporting and improving ML model deployment pipelines and MLOps practices
Monitoring ML model performance in production and building alerting and observability for ML systems
Collaborating with data scientists and product teams to operationalize ML models at scale
Contributing to infrastructure for ML workloads on Kubernetes and AWS

Requirements

What you’ll need

At least 2 years of experience with Amazon Web Services (AWS), with particular focus on EKS, EC2, RDS, Fargate, CloudFront, Lambda, and S3
Extensive hands-on experience using AWS EKS
Experience in direct software engineering following DevOps / SRE practices with at least 1 year as a technical lead
Current ability in at least one of the following languages: Python, Ruby, Elixir, Go, Javascript, Rust
Understanding of container and hypervisor fundamentals
Configuration management (YAML / Bash); experience with Helm and Terraform preferred
Experience running production systems at large scale, and an understanding of the kinds of problems that can occur along with likely solutions
Familiarity with machine learning workflows and MLOps practices
Python experience with ML-adjacent tooling (model deployment, inference serving, or ML pipeline tooling)

Benefits

Comp & perks

Fully remote working environment