DevOps Engineer – Kubernetes & AI Infrastructure

Practicus AI

New

🇬🇧 English

Kubernetes Helm Docker Git Linux Shell scripting Jenkins GitLab CI GitHub Actions TCP/IP DNS TLS Load balancing Persistent volumes CSI Prometheus Grafana Loki Fluent Bit RBAC Secrets management SSO LDAP AWS EKS Azure AKS Google Cloud GKE Istio Terraform Ansible NVIDIA GPU Operator vLLM MLflow Airflow

Job description

About the role

Practicus AI is seeking a hands‑on DevOps Engineer with deep Kubernetes and Linux expertise to deploy, operate, and continuously improve the infrastructure behind its AI platform. You will work across enterprise customer environments, on‑premises and public cloud, ensuring reliable, secure, and scalable AI/ML deployments.

Key responsibilities

Deploy, configure, manage, and troubleshoot Kubernetes clusters across on‑premises and cloud environments.
Install, configure, and support Practicus AI in customer sites, handling GPU‑enabled workloads and LLM inference services.
Administer Linux servers, networking, storage, and container runtimes; automate deployments with Helm, Docker, Git and CI/CD pipelines.
Implement security controls such as RBAC, secrets management, SSO/LDAP integration, and secure ingress.
Set up observability using Prometheus, Grafana, Loki, and Fluent Bit; diagnose complex infrastructure issues.

Required profile

Strong hands‑on experience with Kubernetes administration, deployment, and troubleshooting.
Solid Linux system administration skills, including shell scripting.
Proven ability to investigate and resolve issues across networking, storage, containers, and application layers.
Comfortable working in a collaborative, fast‑paced engineering environment.

Required skills

Kubernetes, Helm, Docker, Git
Linux system administration, shell scripting
CI/CD tools (Jenkins, GitLab CI, GitHub Actions)
Networking fundamentals (TCP/IP, DNS, TLS, load balancing)
Storage concepts (persistent volumes, CSI)
Observability tools (Prometheus, Grafana, Loki, Fluent Bit)
Security practices (RBAC, secrets management, SSO, LDAP)
Cloud platforms (AWS/EKS, Azure/AKS, Google Cloud/GKE) – nice to have
Service mesh (Istio) – nice to have
IaC tools (Terraform, Ansible) – nice to have
GPU workloads (NVIDIA GPU Operator) – nice to have
LLM serving (vLLM) – nice to have
MLOps tools (MLflow, Airflow) – nice to have

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Published 1 hour ago

Expires 1 month from now

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