About the Role
Avenga is an international engineering firm helping businesses operate with AI at the core. With 6,000+ experts worldwide, we combine engineering expertise with AI-native thinking to turn ambitious ideas into real-world impact across industries, technologies, and markets. We're looking for an experienced DevOps Engineer with a strong interest in AI-driven automation to help modernize cloud infrastructure and software delivery. The role combines hands-on experience with AWS, GCP, Azure, CI/CD pipelines, and infrastructure automation with the use of AI tools, autonomous agents, and Model Context Protocol (MCP) to optimize DevOps workflows. You'll play a key role in integrating AI into engineering processes, building smarter automation solutions, and improving delivery efficiency and engineering productivity.
Responsibilities
•Build and maintain CI/CD pipelines using GitLab
•Manage AWS, GCP, and Azure infrastructure and migrations
•Deploy and scale containerized workloads using Docker and Kubernetes
•Automate infrastructure using Terraform/OpenTofu, Python, and Bash
•Integrate APIs, cloud services, and development platforms
•Apply AI tools, autonomous agents, and MCP to DevOps workflows
•Implement cloud security, access controls, and secrets management
•Maintain monitoring, logging, alerting, and incident response
•Develop reusable CI/CD templates and self-service deployment workflows
•Build internal developer tools to improve engineering productivity
Requirements
•5+ years of experience in DevOps, Cloud Engineering, Platform Engineering, or a related field
•Strong experience designing, implementing, and maintaining CI/CD pipelines, particularly with GitLab CI/CD, including automated testing, build processes, deployments, release management, and rollback strategies
•Hands-on experience with AWS, Google Cloud Platform (GCP), and Microsoft Azure, including cloud infrastructure management and migrating applications, workloads, and services between platforms with minimal downtime
•Experience with Docker, Kubernetes, and container orchestration, including deployment management, scaling, service discovery, configuration, and workload reliability
•Proficiency with tools such as Terraform or OpenTofu, alongside scripting languages such as Python or Bash, to automate infrastructure provisioning, configuration, and lifecycle management
•Experience integrating automation workflows with APIs, development platforms, and cloud services
•Active use of AI tools, autonomous agents, and Model Context Protocol (MCP)
•Strong understanding of cloud security, identity and access management, secrets management, monitoring, logging, alerting, vulnerability scanning, and incident troubleshooting
•Experience building reusable CI/CD templates, self-service deployment workflows, standardized environments, and internal developer tooling to improve engineering productivity and delivery efficiency
