DevOps Engineer

We are looking for an experienced DevOps Engineer to help design, operate, and scale highly available platforms. The ideal candidate has strong expertise in observability, automation, and CI/CD, combined with hands-on experience in container orchestration. If you are passionate about reliability, performance, and improving engineering workflows, please review the technical requirements below:

technical requirements

-  Strong expertise in load balancing concepts and High Availability (HA) architectures.
-  Proficiency in the ELK Stack (Elasticsearch, Logstash, Kibana).
-  Expertise in Graylog with solid troubleshooting skills.
-  Hands-on operational experience with Docker Swarm and Kubernetes.
-  Proficiency in GitLab and implementation of CI/CD pipelines.
-  Ability to design, deploy, and maintain automation solutions.
-  Solid programming and scripting skills (for automation and operational tasks).
-  Strong command of monitoring services and Time Series Databases (TSDB).
-  Proficiency in working with queuing services (e.g., RabbitMQ, Kafka).
-  Familiarity with microservices architecture and its core concepts.
-  Familiarity with Agile methodologies and task management tools such as Jira and Confluence.

Soft Skills:

-  Ability to approach unfamiliar situations with structured, logical thinking rather than relying on pre-learned patterns.
-  Capacity to collaborate effectively in environments where priorities evolve, while maintaining alignment with the team.
-  Comfort making decisions under uncertainty, paired with accountability for outcomes.
-  Consistency in maintaining professional, clear communication, especially under time pressure or conflicting viewpoints.
-  Habit of breaking complex problems into smaller, verifiable steps, instead of depending on assumptions.
-  Ability to remain objective during incidents, focusing on observable facts and data.
-  Willingness to challenge assumptions respectfully, supported by reasoning and evidence.
-  Long-term learning mindset, prioritizing conceptual understanding over tool-specific knowledge.
-  Reliability in execution, delivering work with predictability and discipline.
-  Capacity to connect technical details to broader system impact, understanding how local decisions affect overall performance.