Mid DevOps

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions. 

We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.  

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing. 

You will be:

• develop, maintain, and improve cloud-native infrastructure and platform solutions within Microsoft Azure environments,
• build and support Kubernetes-based platforms, preferably using Azure Kubernetes Service (AKS),
• implement and maintain workflow orchestration solutions using technologies such as Argo Workflows, Temporal, Airflow, or similar tools,
• develop Python-based automation scripts, operational tooling, and workflow components,
• support integration and messaging solutions using technologies such as Azure Service Bus, Kafka, Event Hub, or similar event-driven platforms,
• contribute to CI/CD pipelines, GitOps workflows, Infrastructure as Code, and deployment automation practices,
• monitor, troubleshoot, and optimize cloud infrastructure, distributed systems, and containerized applications,
• collaborate with software engineers, DevOps teams, architects, and stakeholders to deliver scalable and maintainable platform solutions,
• support cloud modernization initiatives and continuous improvement of engineering standards and operational practices,
• participate in technical discussions related to cloud-native architecture, automation, scalability, reliability, and observability,
• ensure high quality and operational stability through automation, monitoring, testing, and proactive support activities,
• depending on seniority, take ownership of technical areas, support architectural decisions, and mentor less experienced team members,

Your profile:

• commercial experience working with cloud infrastructure, platform engineering, or DevOps-oriented roles,
• strong hands-on experience with Kubernetes, preferably within Azure Kubernetes Service (AKS) environments,
• solid experience working within Microsoft Azure cloud ecosystems,
• practical experience with workflow orchestration platforms such as Argo Workflows, Temporal, Airflow, or similar technologies,
• strong Python skills for scripting, automation, operational tooling, and workflow development,
• good understanding of integration and messaging systems, including Azure Service Bus, Kafka, Event Hub, or comparable event-driven technologies,
• experience with CI/CD pipelines, Infrastructure as Code, GitOps practices, and deployment automation,
• good understanding of cloud-native architectures and containerized application environments,
• experience with monitoring, observability, logging, and operational support practices,
• strong communication skills and the ability to work effectively with both technical and non-technical stakeholders,
• consultative mindset with the ability to collaborate in complex international environments,
• strong problem-solving skills and proactive engineering attitude,
• team-player mentality with strong collaboration capabilities across distributed engineering teams,
• experience with Git and modern software delivery workflows,
• •  

Nice to have:

• experience working within enterprise-scale cloud transformation initiatives,
• knowledge of distributed systems and event-driven architecture patterns,
• experience with security, compliance, and operational governance in cloud environments,
• familiarity with modern platform engineering practices and internal developer platforms,
• experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work,
• interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches,

• • Practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.

  • Work from the European Union region and a work permit are required.

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision