O pozici
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.
Co budeš dělat
- designing and evolving cloud platforms with a focus on Azure Cloud services,
- leading on-premises to cloud migration initiatives ensuring scalability, security, and high availability,
- implementing automated deployment pipelines and infrastructure provisioning solutions,
- driving platform security testing and ensuring compliance with company-wide security standards,
- partnering with Product, Architecture, and Software Engineering teams to deliver secure, cost-effective SaaS products,
- contributing to platform engineering, operations, and security research to introduce innovative solutions,
- supporting continuous improvement of operational processes critical for cloud platform and operations success,
- aligning work with company-wide OKRs and strategic initiatives focused on cloud-native transformation.
Koho hledáme
- 6+ years of experience in infrastructure, software engineering, or DevOps, with strong expertise in Microsoft Azure Cloud services (IaaS, PaaS, SaaS), including compute, networking, security, data services, and containerization (Azure Kubernetes Service),
- hands-on experience designing and building scalable, multi-region, highly available, secure Azure Landing Zones using the Cloud Adoption Framework (CAF),
- proven track record of migrating on-premises workloads to cloud with approaches beyond lift-and-shift, including rehosting, re-platforming, and refactoring,
- proficient in Infrastructure as Code (Terraform and/or Bicep and/or Pulumi) and scripting/programming languages such as PowerShell, Bash, SQL, and KQL,
- hands-on experience with Azure DevOps pipelines (including YAML), GitHub Actions, CI/CD processes, and integration of security and code quality tools,
- solid background in container orchestration with Kubernetes and Docker, specifically managing Azure Kubernetes environments,
- strong understanding and practical experience implementing cloud security controls, including privileged access management, vulnerability management, secrets management, WAF solutions, security logging, monitoring, and alerting,
- expertise in business continuity planning and disaster recovery solutions for on-premises and cloud-deployed services,
- thorough knowledge of cryptography, certificates (X509), TLS/SSL security parameters, and secure development lifecycle practices,
- experience with security scanning tools (static/dynamic code analysis, container and open-source scanning) and information security best practices in cloud environments,
- practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.
- good verbal and written communication skills in English (min. B2).
- Work from the European Union region and a work permit are required.
Benefity
- Nice to have:
familiarity with Elasticsearch,
knowledge of Linux, package management, and configuration management tools,
background in implementing controls for ISO 27001 and SOC 2,
understanding of OWASP Testing Guide and methodology,
experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work,
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