HPC Platform Engineer - AI

We are an emerging AI infrastructure start-up building next-generation data centres and high-performance compute environments to power AI, LLM training, and cloud-scale workloads, powered by renewable energy, rooted in sovereign capability, and designed to give enterprises and innovators the compute they need. Backed by leading investors, we are rapidly expanding our site development pipeline, engineering capabilities, and commercial partnerships.

We are looking for HPC Platform Engineer who can assist in shaping the new Platform team, this role will be customer facing, involve technical troubleshooting, and collaboration with vendor engineering teams to ensure seamless AI platform operations.  

Key Responsibilities:

• Coordinate resolution of complex issues (L3) to (vendor) product/engineering teams and manage vendor responses.
• Monitor system health, alerts, and customer usage patterns.
• Document solutions/workarounds, create and maintain knowledge, document support procedures. 
• Automate common tasks and fixes. 
• Configure and integrate tooling to support optimal operation of the platform, and support tool selection. 
• Assist customers with platform configuration, onboarding, and usage best practices. 
• Collaborate with platform and infrastructure support/engineering teams to resolve platform integration issues. 
• Ensure SLAs and customer satisfaction targets are met.
• L1 support for customer-reported issues and requests.
• L2 support by diagnosing, replicating, and troubleshooting issues across platform and infrastructure. 
• Work with customers and multiple stakeholders to understand requirements and challenges, provide reporting on usage, workflow and billing. 

Technical responsibilities:

• Cluster Infrastructure management: Managing the Nvidia GPU cluster .
• High availability and resilience: Implement failover strategies and manage maintenance events to minimise downtime.
• Resource allocation and optimisation: Resource partitioning (GPU resources), workload scheduling, capacity planning. 
• Performance monitoring and troubleshooting: Performance analysis, monitoring (realtime) with available Nvidia and HPE tools.  
• Incident response: node failure management, network issues, driver issues, troubleshooting common issues and then working with vendor support to resolve any critical issues. 
• Security and access control: Manage user permissions, RBAC, security hardening, data protection.  

Required Skills & Experience: 

• Extensive experience in technical support, system engineering, or platform operations.
• Solid understanding of L1 and L2 support processes (ticketing, escalation, troubleshooting).
• Familiarity with cloud-based platforms, APIs, and distributed systems.
• Understanding of AI/ML concepts and tooling (model training, inference, data pipelines basics).
• Experience with monitoring/logging tools (e.g., Grafana, Kibana, Splunk). 
• Excellent communication skills to interface with both customers and internal / vendor teams. 
• Good understanding of tools requirements for ML engineers and data scientists, and how to optimize the experience.

Core Technical skills: 

• System administration experience with OS's like RHEL/CentOS, Ubuntu, tuning Linux kernel.
• Proficiency with Ansible, Nvidia and CUDA toolkits, Kubernetes and container orchestration.
• Understanding of automation, monitoring and security with GPU as a service.

Preferred experience:

• Experience supporting HPE PCAI or other AI/HPC infrastructure and platforms. 
• Experience with GPU resource allocation (across instances, GPUs count and time). 
• Advanced networking skills with High performance networking, troubleshooting and fine tuning.
• Background in DevOps or SRE practices. 
• ITIL familiarity. 

Success Metrics: 

• Customers receive timely, effective support with minimal escalations. 
• Issues are resolved or routed correctly with high-quality documentation. 
• The platform maintains strong uptime and customer satisfaction.