Site Reliability Engineer – ML Infrastructure

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TableCheck

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2011

☁️ SaaS

🤝 B2B

SaaS • B2B

TableCheck is a technology company founded in 2011 that provides an all-in-one restaurant booking and guest experience platform for restaurants and hospitality businesses worldwide. It offers SaaS solutions to help restaurants manage reservations, guest interactions, and operational workflows, aiming to improve dining experiences and enable hospitality businesses to connect with diners more effectively. TableCheck operates globally with offices across multiple countries and emphasizes product innovation and sales/consulting services for the hospitality sector.

📋 Description

• 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

• 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