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Platform DevOps Engineer (K/M)
Build and run the Kubernetes-based SaaS platform’s core infrastructure, automating deployments, observability, and security for a new product from the ground up.
DevOps Engineer (AI-native Stack)
Build and operate a multi-cloud, AI-native platform from scratch, designing Kubernetes infrastructure, CI/CD pipelines, and AIOps workflows using tools like GitLab, ArgoCD, and Prometheus/Grafana.
Devops Engineer (Kubernetes, Openshift) @ Antal
DevOps Engineer designs and implements Kubernetes/OpenShift-based cloud-native environments, migrates apps to containers, and automates CI/CD pipelines for a fintech client.
DevOps engineer for cloud platform service
Builds and maintains a private cloud platform using Git, Jenkins, Docker, Linux, and optionally Java/Python/Go for CI/CD and automation.
Senior DevOps Engineer at EPAM Systems
Senior DevOps Engineer builds and maintains CI/CD pipelines, Kubernetes clusters, and Azure cloud infrastructure using Python and GitLab CI/CD to automate deployments and optimize cloud costs.
DevOps Engineer (AWS Migration Project)
Build and migrate AWS infrastructure from scratch using Terraform, Docker, and Kubernetes while automating CI/CD pipelines and ensuring security and high availability.
Sr. Systems Administrator
Senior Systems Administrator designs and maintains enterprise observability and monitoring stacks (OBM, NNMi, Zabbix, Prometheus, Graylog) across hybrid/cloud platforms, automates with Python/Bash/Terraform/Ansible, and leads incident response and SRE practices.
DevOps Engineer (M/F)
Design and maintain enterprise CI/CD pipelines using Jenkins and Harness, automate deployments with Groovy and Bash, and collaborate on cloud-native DevOps practices for a fintech client.
DevOps Engineer @ Grape Up
Maintain and scale an AWS-based Kubernetes platform using Terraform and CI/CD, ensuring reliability and observability for large-scale data processing.
Data and AI DevOps Engineer
Design and maintain CI/CD pipelines for data and AI workloads using cloud-native tools like Kubernetes and Azure, while collaborating with data engineers to optimize AI systems and Gen AI/LLM solutions.
Data and AI DevOps Engineer
Design and maintain CI/CD pipelines for data and AI workloads on Azure, automate infrastructure with IaC, and optimize data pipelines using Kubernetes, Databricks, and related tools.
DevOps Engineer (m/f/d)
Maintains and automates CI/CD pipelines for travel software, using Docker, Kubernetes, Jenkins, and monitoring tools like Prometheus/Grafana.
Senior DevOps Engineer – Application Deployment
Senior DevOps Engineer designs and automates cloud infrastructure and CI/CD pipelines using Terraform, AWS, Azure, and GitHub Actions to deploy containerized apps on Kubernetes.
Platform DevOps Engineer (K/M)
Build and run a Kubernetes-based PaaS platform for multi-tenant clients, automating deployments with GitOps, securing the stack, and writing Go operators to extend platform behavior.
Platform DevOps Engineer (K/M)
Build and run a Kubernetes-based PaaS platform: design multi-tenant clusters, automate GitOps deployments with FluxCD, write Go operators, and secure on-premise infrastructure for production workloads.
Gcore: DevOps Engineer (Cloud AIaaS)
Design and maintain Kubernetes-based infrastructure for scalable AI inference workloads, including GPU scheduling and monitoring, using tools like Terraform, Ansible, and Prometheus.
Java Software Engineer
Builds and extends a mission-critical emergency-call platform using Java/Kotlin, Kubernetes, and cloud services, while coaching teams on software craftsmanship.
Stackly-AWS DevOps Engineer
Designs and maintains AWS cloud infrastructure, automates CI/CD pipelines, and ensures secure, scalable deployments using Terraform, Kubernetes, and monitoring tools.
Formamind Senior DevOps Engineer
Senior DevOps engineer designs and maintains scalable cloud infrastructure, CI/CD pipelines, and observability stacks using Kubernetes, Terraform, and AWS/GCP/Azure.
DevOps Engineer (AI, GitLab)
Build and maintain AI infrastructure and MLOps pipelines using GitLab, Docker, Kubernetes, and Terraform to deploy and scale AI models in cloud or hybrid environments.