A fully integrated Restaurant Booking & Table Management System built to help restaurants enhance their guest experience
Site Reliability Engineer – ML Infrastructure
Location
Japan
Posted
2 days ago
Salary
0
Seniority
Mid Level
Job Description
Site Reliability Engineer – ML Infrastructure
TableCheck
• 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
Job Requirements
- 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
- Fully remote working environment
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