Job Description

Job Summary:

We are looking for an experienced Infrastructure Engineer to design, implement, and optimize data center and storage solutions. The ideal candidate brings solid hands-on expertise with software-defined storage, container orchestration, and infrastructure automation. You'll work collaboratively to deliver scalable, reliable storage and compute infrastructure while contributing to AI/ML workload enablement.

Key Responsibilities:

• Design and implement software-defined storage solutions using Ceph, focusing on performance, scalability, and reliability
• Deploy and manage Kubernetes clusters for containerized workloads and storage orchestration
• Automate infrastructure provisioning and configuration using IaC tools (Juju, Terraform)
• Optimize storage performance metrics including IOPS, latency, and throughput for diverse workload requirements
• Participate in the solution lifecycle: requirements analysis, design, implementation, testing, and ongoing optimization
• Deploy and configure hardware infrastructure including servers, storage systems, and networking components
• Collaborate with DevOps, application, and platform teams to ensure infrastructure meets business and technical needs
• Monitor and troubleshoot infrastructure issues; implement improvements based on performance analysis
• Stay current with storage technologies, container ecosystems, and infrastructure best practices

Job Requirements

Required Qualifications:

• 5-7 years of experience in infrastructure engineering with focus on storage and data center technologies
• Strong hands-on experience with Ceph storage (block, object, and file storage modes)
• Proficiency with Kubernetes including storage integration (CSI drivers, persistent volumes, StatefulSets)
• Solid experience with Infrastructure as Code using Terraform and/or Juju
• Understanding of storage performance concepts: IOPS, throughput, latency tuning, and capacity planning
• Experience with Linux system administration and storage protocols (iSCSI, NFS, RBD, S3)
• Practical deployment experience: server configuration, storage setup, and system integration
• Good communication skills with ability to document architectures and explain technical concepts
• Relevant certifications a plus: CKA (Certified Kubernetes Administrator), Linux certifications, or vendor-specific storage certifications

Preferred Qualifications:

• Experience supporting AI/ML workloads and understanding their storage requirements (high IOPS, parallel access patterns)
• Knowledge of storage backends for AI frameworks (distributed storage, GPU-optimized data pipelines)
• Familiarity with monitoring and observability tools (Prometheus, Grafana)
• Experience with automation frameworks (Ansible, Python scripting)
• Experience with Golang for Kubernetes plugin
• Understanding of software-defined networking (SDN) and container networking
• Exposure to hybrid cloud or multi-cluster environments

Skills