We’re currently hiring a DevOps Engineer (Kubernetes administration) for one of our direct clients at San Jose, CA. I’ve included the job description below, if this aligns with your experience I’d love to set up a quick call to discuss this opportunity in more detail.
Job Title: DevOps Engineer (Kubernetes administration)
Work Location: San Jose, CA
The client is looking specifically for candidates with extensive Kubernetes administration experiences beyond traditional DevOps.
THE PERSON:
- Experience in Platform, Infrastructure, DevOps Engineering.
- Deep hands-on experience with Kubernetes and container orchestration at scale.
- Proven ability to design and deliver platform features that serve internal customers or developer teams
- Experience building developer-facing platforms or internal developer portals (e.g. Custom workflow tooling).
KEY RESPONSIBILITIES:
- Build and extend platform capabilities to enable different classes of workloads (e.g., Large-scale AI training, inferencing etc).
- Design and operate scalable orchestration systems using Kubernetes across both on-prem and multi-cloud environments.
- Develop platform features such as pre-flight health checks, job status monitoring and post-mortem analysis.
- Partner with development teams to extend the GPU developer platform with features, APIs, templates, and self-service workflows that streamline job orchestration and environment management.
- Apply expertise in storage and networking to design and integrate CSI drivers, persistent volumes, and network policies that enable high-performance GPU workloads.
- Production support on large-scale GPU clusters.
PREFERRED EXPERIENCE:
- Hands-on experience in storage or network engineering within Kubernetes environments (e.g., CSI drivers, dynamic provisioning, CNI plugins, or network policy).
- Experience with Infrastructure as Code tools like Terraform.
- Background in HPC, Slurm, or GPU-based compute systems for ML/AI workloads.
- Practical experience with monitoring and observability tools (Prometheus, Grafana, Loki, etc.).
- Understanding of machine learning frameworks (PyTorch, vLLM, SGLang, etc.).
- High performance network and IB/RDMA tuning.
ACADEMIC CREDENTIALS:
Bachelor’s or master's degree in computer science, computer engineering, electrical engineering, or equivalent.
