Engineer, Cloud

Chipotle•Columbus, OH

5d•$87,000 - $120,000

About The Position

The Engineer – Platform Engineering (Cloud Engineer) builds and manages Chipotle’s cloud and on-premises infrastructure, with a focus on automation and scalability. The role implements and maintains core platform services—including cloud resources, servers, and CI/CD pipelines—that support restaurants and enterprise systems. Using Infrastructure-as-Code and robust monitoring, the engineer improves reliability, performance, and security while contributing to cloud adoption and platform innovation as part of Chipotle’s modernization initiatives and Infrastructure Strategy.

Requirements

Bachelor’s degree (BA/BS) from a four-year college or university.
Three to five years of experience.
Hands-on experience with Azure or AWS cloud infrastructure, virtualization, and containerization, and a strong understanding of servers, storage, networks, and security.
Proficiency in scripting and automation, such as Terraform, PowerShell, and CI/CD pipelines, to build and manage Infrastructure-as-Code.
Hands-on experience with leading LLMs and AI coding tools, including using AI to generate, review, and validate Infrastructure-as-Code and automation; understanding of hybrid AI architectures.
Strong analytical and troubleshooting abilities, including the ability to diagnose complex infrastructure problems under pressure and implement reliable solutions.
Effective communication and teamwork skills, including cross-functional collaboration and clear documentation of technical processes.
A security-minded, detail-oriented approach, including adherence to cloud governance, identity and access management, and compliance best practices.

Responsibilities

Configure, deploy, and manage Azure or AWS cloud infrastructure and on-premises servers in accordance with security and performance best practices.
Develop and maintain Infrastructure-as-Code templates and CI/CD pipelines, including Terraform scripts and Azure DevOps tasks, to automate resource provisioning and configuration.
Leverage leading LLMs and hybrid AI models to accelerate engineering work, including generating and validating Infrastructure-as-Code, scripts, automation, and troubleshooting workflows.
Implement and tune monitoring, logging, and alerting solutions to detect platform issues and optimize performance.
Troubleshoot complex infrastructure issues involving networks, operating systems, or cloud services; work with cross-functional teams to restore service and identify root causes.
Research and introduce technologies or tools, such as containers, configuration management, and AIOps platforms, to improve infrastructure efficiency, scalability, or cost-effectiveness.