DevOps Engineer (On-Premise Infrastructure, AI-driven approach)

We are looking for a DevOps Engineer (Middle+ / Senior) to join a product company with a complex infrastructure environment and a growing focus on using AI to improve DevOps workflows and delivery speed.

This role is hands-on and involves working with on-premise infrastructure, containerization, and CI/CD, while also exploring and applying modern AI tools to automate and accelerate infrastructure-related tasks.

Responsibilities
Design, build, and maintain on-premise infrastructure
Work with containerized environments (Docker, Kubernetes, LXC)
Support and operate multi-layer infrastructure (bare metal, virtualization, containers)
Adapt infrastructure setups for different client environments and requirements
Develop and maintain Infrastructure as Code
Improve and support CI/CD pipelines (GitLab)
Participate in infrastructure migrations and zero-downtime deployments
Configure and maintain monitoring, logging, and alerting systems
Troubleshoot issues across infrastructure, networking, and application layers
Collaborate with development teams to improve delivery processes
Leverage AI tools (LLMs, code generation, automation tools) to:
speed up infrastructure development
simplify integration with new services and APIs
improve automation and internal tooling
Requirements
4–6+ years of experience in DevOps / Infrastructure / SRE roles
Strong hands-on experience with:
Kubernetes
Docker / container runtimes
Linux-based systems (Debian/Ubuntu)
Experience with virtualization (KVM or similar)
Experience working with on-premise or hybrid infrastructure
Hands-on experience with CI/CD pipelines (GitLab or similar)
Experience with Infrastructure as Code (Python or similar)
Understanding of networking and load balancing (L4/L7, Nginx)
Experience with databases (MySQL / replication is a plus)
Strong troubleshooting and problem-solving skills
Ability to work independently and take ownership
English level: B2 or higher
AI-focused expectations
Practical experience using AI tools in daily work (e.g., code generation, automation, working with documentation)
Understanding of how LLMs can be applied in DevOps workflows
Ability to quickly learn and adopt new AI tools and approaches
Interest in improving engineering efficiency using modern technologies