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Highrise AI, a GPU-native infrastructure platform subsidiary of Hut 8 Corp., is seeking a mid-level AI Infrastructure Operations Engineer to provide Tier 2 operational support for large-scale GPU compute and storage platforms. This hands-on role focuses on owning technical tickets, restoring service, investigating recurring issues, and maintaining reliable day-to-day operation of production AI clusters.
You will work closely with the Network Operations Center (NOC), on-site data hall technicians, SRE teams, and senior infrastructure engineers to troubleshoot Linux-based compute nodes, GPU systems, storage services, and their supporting software. Complex or systemic issues will be escalated to Tier 3 engineering with clear diagnostics and supporting evidence.
Key Responsibilities:
Tier 2 Incident and Ticket Management:
- Own and manage escalated incidents, service requests, and problem tickets from initial technical triage through resolution, escalation, or documented handoff
- Diagnose compute and storage faults using system logs, monitoring dashboards, management interfaces, and approved diagnostic tools
- Assess severity and customer impact; coordinate with the NOC and site teams to restore service within operational and SLA targets
- Produce clear troubleshooting records, timely ticket updates, shift handovers, and actionable Tier 3 or OEM escalations
Compute and GPU Platform Operations:
- Troubleshoot Linux servers, GPU health alerts, boot failures, firmware/driver issues, hardware faults, and resource availability problems
- Support node provisioning, reimaging, configuration changes, routine maintenance, and return-to-service validation using established automation and runbooks
- Investigate Kubernetes and Slurm node or workload failures, working with platform engineers when application or scheduler-level escalation is required
- Perform first-level analysis of connectivity or GPU communication symptoms and engage network or Tier 3 engineers for deeper fabric and performance investigation
Highrise AI combines bare-metal performance with full-stack orchestration, API-driven access, and hardware-enforced isolation to support training, fine-tuning, and production-scale deployment of AI models. The platform integrates disciplined operations, advanced automation, and on-site engineering expertise to sustain high uptime and predictable performance across deployments, including managed services for third-party GPU hardware.
You will work with bright, driven peers from diverse educational and professional backgrounds including software development, energy, engineering, entrepreneurship, investment banking, private equity, and management consulting. The environment emphasizes meritocratic debate, steep learning curves, and building a lifelong network at the intersection of technology, energy, and infrastructure.
Requirements:
The posting does not explicitly state formal requirements such as years of experience, required certifications, or educational credentials in the provided excerpt. However, the role assumes proficiency with Linux system administration, GPU troubleshooting, monitoring tools, and familiarity with Kubernetes and/or Slurm workload schedulers. Experience with incident management, technical documentation, and cross-functional collaboration is expected.
