Senior Manager, AI Infrastructure Operations
New
V
VultrCloud Infrastructure
Remote - United StatesFull-TimeManager
Salary150,000 - 160,000 USD per year
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Job Details
- Experience
- 6–10 years of experience
- Required Skills
- KubernetesLinuxTerraformAnsibleDistributed Systems
Requirements
- 6–10 years of experience in infrastructure engineering, HPC, large-scale systems, or similar fields.
- Strong understanding of AI compute infrastructure, including GPU/CPU clusters, distributed training architectures, and high-performance networking (InfiniBand/RDMA).
- Experience running production bare metal, GPU, or hardware fleet operations at meaningful scale.
- Hands-on expertise with Linux systems.
- Hands-on expertise with Kubernetes or Slurm.
- Hands-on expertise with provisioning tools (Terraform, Ansible).
- Hands-on expertise with observability platforms and networking fundamentals.
- Proven track record in cluster operations, hardware bring-up, distributed systems, or ML workload support.
- Experience leading engineering teams or pods, with the ability to manage execution while staying close to technical work.
- Ability to communicate effectively with cross-functional engineering teams and translate strategy into actionable engineering tasks.
- Strong execution mindset with the ability to prioritize, deliver, and adapt in a fast-paced environment.
Responsibilities
- Lead the engineering team responsible for the day-to-day implementation, scaling, and operation of AI compute clusters.
- Translate engineering roadmaps and technical requirements from the Director of AI Infrastructure into detailed project plans and execution milestones.
- Drive delivery of cluster deployments, hardware bring-up, node configuration, and integration with orchestration and scheduling systems.
- Ensure cluster reliability, uptime, and performance through monitoring, automation, and continuous operational improvements.
- Oversee lifecycle operations for bare metal and GPU fleets, including provisioning, configuration management, firmware/driver updates, and hardware validation.
- Manage incident response for GPU and cluster infrastructure, ensuring timely resolution and root-cause analysis.
- Work closely with AI/ML, SRE, Networking, and Hardware Engineering teams to ensure cluster capabilities meet training and inference needs.
- Coach and mentor engineers, fostering a high-performance, detail-oriented engineering culture.
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