DevOps Engineer

Global-e (Nasdaq: GLBE) is the world’s leading platform for global direct-to-consumer e-commerce, enabling brands and retailers to seamlessly sell worldwide, delivering localized experiences across markets.

Trusted by some of the world’s most recognized brands including Adidas, Alo Yoga, Logitech, Hugo Boss, Marc Jacobs, and Victoria’s Secret, we operate on a massive global scale through dynamic teams across North America, EMEA, and APAC.

Our technology-based end-to-end solution combines advanced localization capabilities, streamlined global operations and extensive big-data analytics, covering everything merchants need to scale worldwide.

We’re a fast-moving, highly collaborative company where innovation, creativity, ownership, and impact are part of everyday work.

Key Responsibilities:

• Establish and enforce industry-leading practices for Continuous Integration and Continuous Deployment (CI/CD), particularly tailored for microservices architecture.
• Analyze current systems, design innovative solutions, and build new features to optimize performance and reliability.
• Maintain and update our Kubernetes (K8S) clusters to ensure seamless operation and scalability.
• Build, manage, and enhance our infrastructure to guarantee reliability and scalability in our production environment.

Requirements:

• At least 4 years of experience as a DevOps Engineer with a robust background in infrastructure management.
• At least 3 years of practical experience with Kubernetes and AWS services, including RDS, Lambda, S3, VPC, CloudWatch, and more.
• Experience in overseeing and maintaining production environments.
• Proficient in network infrastructure, Linux systems, firewall configurations, DNS, and CDN tools.
• Experience with automation tools such as Ansible and Terraform to streamline processes.
• Solid understanding of databases, particularly SQL and RDS.
• Strong coding skills in Python, PowerShell, Ansible, Shell, and Groovy.

AI Knowledge Requirements:

• Familiarity with modern AI/ML concepts and frameworks, including hands-on experience with MCP, agentic systems and multi-agent orchestration, Retrieval-Augmented Generation (RAG) pipelines, prompt engineering.
• AI infrastructure integration into CI/CD workflows, cloud environments, and scalable DevOps automation.