Strong fit for infrastructure engineers who build automation and manage cloud-native systems, with a focus on GCP and Kubernetes.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Poland and Middle-Eastern Europe via Greenhouse
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 17 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 ING, Millenium Bank, Play, Wavin, Arabian Drilling, FedEX, Leroy Merlin, Edenred, 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:
designing, building, and maintaining reliable, scalable, and secure infrastructure services,
developing automation and tooling to simplify infrastructure provisioning, management, and scaling,
improving infrastructure performance, availability, and resilience as systems and business needs evolve,
monitoring system health, identifying potential issues, and implementing improvements to ensure service reliability,
investigating infrastructure incidents, performing root cause analysis, and driving corrective actions,
collaborating with application development teams on monitoring, capacity planning, and infrastructure improvements,
implementing effective observability, alerting, and incident management practices,
contributing to infrastructure architecture decisions and continuous improvement initiatives.
Your profile:
have extensive experience in Linux system administration, preferably in high-throughput and low-latency environments,
practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery,
have strong hands-on experience with GCP services, particularly networking, IAM, compute, storage, and Kubernetes, or transferable experience with AWS or Azure,
have extensive practical knowledge of Docker and Kubernetes, including deploying, scaling, and monitoring Kubernetes clusters,
understand distributed systems architecture and the challenges of operating services across processes and geographical locations,
have hands-on experience with service orchestration, deployment, configuration management, and infrastructure automation,
understand process isolation, containerization, and container orchestration concepts,
have experience with monitoring and observability tools such as Prometheus, Grafana, Stackdriver, Datadog, or New Relic,
have practical experience with incident management, troubleshooting, root cause analysis, and improving system reliability,
understand cloud security best practices, secrets management, and basic compliance requirements,
have a good understanding of the software development lifecycle, including version control, building, testing, staging, and deployment,
have a strong Continuous Delivery mindset and understand infrastructure lifecycle management,
work from the European Union region and a work permit are required.
Nice to have:
have experience developing and maintaining Kubernetes operators,
have experience deploying and scaling distributed databases and data stores such as Apache Cassandra, ScyllaDB, MySQL, PostgreSQL, Redis, or Memcached,
have experience with Go or a strong willingness to learn it for developing and improving Kubernetes operators,
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.
Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision
Source: Poland and Middle-Eastern Europe careers (Greenhouse)