The Technology Platform Engineer will design and implement high-performance computing (HPC) and AI infrastructure solutions. This role focuses on creating production and non-production cloud environments, deploying automation pipelines, and managing XPU-based clusters (CPU/GPU/accelerators) to provide Metal as a Service (MaaS), GPUaaS, and AIaaS offerings. Key responsibilities include optimizing cluster performance, scalability, and cost efficiency across on-premises, cloud, and hybrid environments, as well as integrating AI/HPC platforms with existing IT systems and security frameworks. The engineer will monitor and tune infrastructure for high availability and low-latency networking, while providing technical guidance for large-scale models and simulations. Required qualifications include at least 5 years of experience in enterprise-wide HPC strategy and supercomputing environments, proficiency in Linux architecture, advanced CUDA/GPU management (H100/A100), and cluster design (SLURM/PBS Pro). Candidates must have hands-on experience with accelerated computing architectures, high-performance fabrics, and cluster orchestration tools like Kubernetes and Run:ai. Preferred qualifications include experience managing 1,000+ GPU clusters, knowledge of AI frameworks (TensorFlow, PyTorch, JAX), and familiarity with Agentic AI technologies for workflow automation. An educational background of 15 years of full-time education is required.
For full details about this role, click “Apply on company site” to view the employer’s original posting.
Skills for this role
Linux ArchitectureHPCGPUCUDASLURMPBS ProMPIOpenMPSYCLNVIDIA DGX SuperPODInfiniBandRoCEv2MLOpsKubeflowSingularityKubernetesAnsibleTerraformUFM managementRunAIAzure MLMLflowBicepPythonMachine LearningDockerEdge ComputingAgentic AI
Your skill match
Checking your profile…
YOUR NEXT STEP
Get interview-ready for this role
A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Large-Scale XPU Cluster Management
The role requires designing and managing high-performance computing clusters using H100/A100 GPUs and orchestrators like Slurm, Kubernetes, and Run:ai. Success depends on your ability to maintain high availability and performance for massive AI/HPC workloads, specifically in environments scaling to 1,000+ GPUs.
Hybrid Infrastructure Automation
You must implement Infrastructure-as-Code (IaC) using Terraform and Ansible to bridge on-premises supercomputing environments with cloud providers. The priority is creating automated, reproducible pipelines that support MaaS, GPUaaS, and AIaaS offerings across hybrid architectures.
High-Performance Networking and Storage Integration
Optimizing low-latency networking (InfiniBand/RoCEv2) and high-throughput storage (Lustre/GPFS/NVMe-oF) is critical. You are expected to integrate these complex hardware fabrics with existing IT security frameworks and data pipelines to ensure workload resiliency.
Likely challenges to think through
Balancing Performance with Cost Efficiency
A significant challenge is optimizing cluster performance while managing costs through FinOps strategies like Spot VM usage and Reserved Instances. Prepare by demonstrating how you have balanced the high resource demands of AI training with budgetary constraints in previous enterprise-scale projects.
Complexity of Multi-Vendor Integration
Integrating diverse AI/HPC platforms with legacy IT systems and security frameworks often leads to compatibility issues. Prepare by discussing your methodology for troubleshooting bottlenecks in multi-planar networking designs and your approach to maintaining security compliance in high-performance environments.
03
Practice your answers
Technical practice
HPC Cluster Scheduling and Orchestration
What to coverExplain the trade-offs between using SLURM for traditional HPC workloads versus Kubernetes with Run:ai for AI-centric containerized workloads. Discuss how you would integrate these to manage a hybrid environment effectively.
GPU Cluster Performance Optimization
What to coverWhen managing large-scale H100/A100 clusters, what metrics do you prioritize to identify bottlenecks in distributed training? Suggest strategies for optimizing GPU utilization and minimizing communication overhead in multi-node setups.
High-Performance Networking Design
What to coverCompare InfiniBand NDR 400Gb/s and RoCEv2 in the context of multi-rail cluster designs. Describe the specific challenges of maintaining low-latency networking in a large-scale AI infrastructure.
Infrastructure as Code (IaC) for HPC
What to coverHow do you apply Terraform or Ansible to manage configuration baselines for heterogeneous HPC clusters? Provide an example of how you ensure consistency between on-premises and cloud-based burst environments.
Storage Architectures for AI Workloads
What to coverDiscuss the requirements for storage systems like Lustre, GPFS, or NVMe-oF when supporting high-throughput AI training. How do you balance performance, cost, and data locality for large-scale simulations?
Containerization in Supercomputing
What to coverExplain the role of Singularity versus Docker in an HPC environment. Address security and performance considerations when running containerized AI workloads on shared supercomputing resources.
MLOps and Pipeline Integration
What to coverDescribe how you would integrate Kubeflow with existing HPC scheduling systems to automate the end-to-end machine learning lifecycle. Focus on how to handle data pipeline dependencies and resource allocation.
Agentic AI for Infrastructure Automation
What to coverHow can Agentic AI technologies be leveraged to improve observability and automated remediation in a complex cluster environment? Suggest a practical use case for an autonomous agent in a 1,000+ GPU cluster.
Behavioural practice
Managing Large-Scale Infrastructure Incidents
What to coverDescribe a situation where you had to troubleshoot a critical performance degradation in a high-performance computing cluster. Detail the steps you took to isolate the issue and the impact of your resolution.
Cross-Functional Collaboration
What to coverProvide an example of a time you had to explain complex infrastructure requirements to non-technical stakeholders or users. How did you ensure their needs were met while maintaining system stability?
Adapting to Rapid Technological Shifts
What to coverShare an experience where you had to quickly learn and implement a new technology or architecture to support a project. How did you manage the learning curve while meeting project deadlines?
Balancing Performance and Cost
What to coverDescribe a scenario where you had to make a trade-off between high-performance requirements and budget constraints. How did you justify your design decisions to leadership?
02
Sharpen your knowledge
Topics to prepare
HPC Cluster Architecture and Scalability
Focus on the design principles of large-scale XPU clusters, specifically multi-rail InfiniBand/RoCEv2 networking and storage integration (Lustre/GPFS). Exercise: Draft a high-level architecture diagram for a 1,000+ GPU cluster, detailing how you would manage network congestion and storage throughput for distributed training workloads.
Infrastructure-as-Code (IaC) for Hybrid HPC
Understand the lifecycle management of hybrid cloud environments using Terraform and Ansible. Exercise: Create a mock Terraform module that provisions a virtual cluster node and uses an Ansible playbook to configure SLURM and NVIDIA drivers, ensuring consistency between on-premises and cloud environments.
Skills to revise
Advanced CUDA and GPU Management
Deepen knowledge of H100/A100 architecture and performance tuning. Exercise: Review NVIDIA DCGM (Data Center GPU Manager) documentation and simulate a troubleshooting scenario where you identify a bottleneck in a multi-GPU training job using profiling tools.
Cluster Orchestration with SLURM and Run:ai
Master the scheduling and resource allocation for AI/HPC workloads. Exercise: Write a SLURM submission script that requests specific GPU resources and constraints, and explain how you would integrate this with Run:ai to manage multi-tenant GPU quotas.
Linux Kernel and Performance Tuning
Focus on low-latency networking and system-level optimization. Exercise: Practice tuning Linux kernel parameters (sysctl) to optimize TCP/IP stack performance for high-speed interconnects and explain the impact of NUMA topology on HPC application performance.
04
Your preparation checklist
Prepare a technical case study on GPU cluster management
Develop a detailed walkthrough of a past project where you managed large-scale XPU clusters. Focus on your experience with H100/A100 GPUs, SLURM/PBS Pro scheduling, and how you addressed performance bottlenecks or high-availability requirements in a production environment.
Document your infrastructure-as-code (IaC) methodology
Create a summary of how you use Terraform, Ansible, or Bicep to automate the deployment of HPC environments. Be prepared to explain how you maintain configuration baselines and ensure consistency across on-premises, cloud, and hybrid setups.
Review high-performance networking and storage architectures
Prepare to discuss your hands-on experience with InfiniBand NDR, RoCEv2, and parallel file systems like Lustre or GPFS. Explain how you design low-latency networking for distributed training workloads and how you integrate these with AI frameworks like PyTorch or JAX.
Formulate questions regarding AI infrastructure scale
Prepare thoughtful questions for the interviewers about their current cluster scale, specifically regarding the management of 1,000+ GPU environments, their strategy for handling burst workloads between on-prem and cloud, and how they currently implement Agentic AI for infrastructure observability.
Synthesize your MLOps and orchestration expertise
Prepare to explain your workflow for integrating Kubeflow, Run:ai, and containerization tools like Singularity or Docker. Highlight how you enable AIaaS and GPUaaS offerings for end-users while maintaining security frameworks and cost-efficiency.
Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy
Share this opportunityKnow someone who would be a great fit?