Senior DevOps Engineer

O projekcie: Hi there!  We are STX Next, a global IT consulting company specializing in customer-focused software services. Join a group of 500 professionals dedicated to helping customers build outstanding products. Leveraging the latest advancements in the field, and a passion for innovation, we're shaping the future of technology one project at a time. Are you the NEXT one?  What it’s like to be a DevOps Engineer at STX Next As a DevOps Engineer, you’ll join a high-impact project focused on deploying and maintaining a modern, containerized infrastructure to support machine learning workflows. The stack will run on Kubernetes clusters hosted on OpenStack. While the underlying OpenStack environment is already in place, you will be responsible for validating integrations, deploying Kubernetes, and ensuring infrastructure readiness for production-grade ML workloads. Your work will directly support data teams and engineers by delivering scalable, automated, and secure environments tailored for machine learning and advanced analytics. Wymagania: Must have: - Strong knowledge of Kubernetes (solid experience in designing, deploying, and managing Kubernetes clusters in private cloud and/or on bare metal) - Experience in deploying containerized applications using Helm / Operators - Solid hands-on experience with OpenStack (compute, networking, storage, identity) - Familiarity with distributed storage systems (ideally Ceph) and storage integration in Kubernetes - Experience with some IAM tools such as Keycloak or similar - Understanding of machine learning infrastructure needs (e.g., experiment tracking, orchestration, model deployment) - Proficiency with Infrastructure-as-Code tools such as Terraform and/or Ansible - Strong Linux administration skills and scripting (Bash, Python, Go) - Familiarity with Git-based CI/CD pipelines and DevOps best practices - Ability to work in the team but also independently and take ownership of cloud environments Codzienne zadania: - Deploying and configuring Kubernetes clusters in a private cloud. - Automating deployment and configuration of ML-supporting systems within Kubernetes. - Validating and integrating an existing OpenStack-based infrastructure. - Creating Infrastructure-as-Code using Terraform and/or Ansible. - Ensuring scalability, observability, and security across the entire platform. - Collaborating with developers, data scientists, and ML engineers. - Documenting architecture, deployment procedures, and operational best practices.