Technical Manager - GPU Cloud & AI Infrastructure

Risewave Consulting, Inc.; Kuala Lumpur; On-site; MYR 180,000 - 280,000; Full time

Job summary

Risewave Consulting, Inc. in Kuala Lumpur is seeking an experienced Platform Engineer to lead end-to-end GPU compute architecture, HPC and AI infrastructure initiatives for enterprise workloads.

You will drive Kubernetes-based GPU scheduling, high-speed interconnects, and scalable cloud services while collaborating with product and sales teams to define roadmaps. The role demands hands-on GPU cluster experience, Linux/Kubernetes proficiency, and the ability to mentor a growing team as the

Qualifications

• Bachelor’s degree or above in Computer Science, Electronics, or related field.
• 5+ years in Cloud Computing, HPC, AI Infrastructure, or Data Centre Engineering.
• Hands-on 2+ years with GPU clusters, AI cloud platforms, or large-scale compute environments.
• Strong Linux, Docker, Kubernetes, Helm, and cloud-native ecosystems knowledge.
• Experience with Kubernetes GPU scheduling or Slurm cluster management.

Responsibilities

• Drive end-to-end architectural design and reviews for GPU servers and AI compute clusters.
• Assess hardware, interconnects, storage, and network topologies for performance.
• Lead platform operations, SLA management, and multi-tenant resource governance.
• Deploy and maintain GPU resource scheduling/orchestration platforms (Kubernetes/Slurm).
• Collaborate with product and sales to translate requirements into technical roadmaps.
• Support LLM training, fine-tuning, and inference deployment for enterprise workloads.

Skills

Cloud computing

HPC

AI infrastructure

Linux

Kubernetes

Docker

Vendor management

Bilingual (English/Mandarin)

Communication

Team leadership

Education

Bachelor's degree in CS/EE/related

Tools

Kubernetes scheduling

Slurm

DCGM

Prometheus

Grafana

Job description

• Drive end-to-end technical architecture design and review for GPU servers and AI compute clusters.
• Participate in the evaluation and selection of mainstream high-performance GPU hardware platforms and server architectures.
• Assess server topologies, CPUs, GPUs, system memory, high-speed local NVMe storage, and network interfaces.
• Design and review high-speed networking fabrics, including InfiniBand, RoCE, and high-speed Ethernet topologies.
• Evaluate NVLink, NVSwitch, RDMA, and multi-node/multi-GPU interconnect architectures.
• Work with data centre teams to define power density, liquid/air cooling, PDU distribution, network topology, and cabling specifications.
• Establish standard operating procedures for cluster deployment, benchmark testing, acceptance, and expansion.
• Steer the technical strategy and buildout of GPU resource pools, compute clusters, and cloud platform services.
• Deploy and maintain GPU resource scheduling and orchestration platforms built on Kubernetes and/or Slurm.
• Manage GPU drivers, parallel acceleration libraries, container runtimes, GPU Operators, and underlying software stacks.
• Architect multi-tier product offerings spanning Bare Metal, Virtual Machines, Containerized Instances, and On-Demand GPU slots.
• Implement full-card allocation, MIG slicing, GPU sharing, and multi-tenant resource isolation.
• Advance the development of user access control, quota management, automated provisioning, billing integration, and API services.
• Collaborate closely with product and business teams to bring standardized GPU Cloud products to market.
• Support enterprise customers with LLM training, fine-tuning, inference deployment, and performance optimization.
• Analyze customer workload specifications, including model parameter scales, dataset size, concurrency, throughput, and latency SLAs.
• Recommend optimal GPU models, cluster node counts, network fabrics, and storage configurations tailored to client workloads.
• Support mainstream AI frameworks (PyTorch, TensorFlow) and distributed training frameworks.
• Support high-performance inference frameworks and microservices such as vLLM, Triton, TensorRT-LLM, and AI microservice architectures.
• Lead proofs-of-concept (PoCs), performance benchmarking, and technical acceptance testing.
• Platform Operations & SLA Management
• Build comprehensive monitoring and alerting systems across GPUs, compute nodes, network switches, and storage (e.g., DCGM, Prometheus, Grafana).
• Track GPU utilization, VRAM usage, power consumption, thermal profiles, ECC hardware errors, and idle resource rates.
• Implement metering logic based on GPU-hours, instance-hours, or token generation metrics.
• Define Service Level Agreements (SLAs), incident severity levels, escalation pathways, and disaster recovery plans.
• Continuously optimize platform uptime, compute efficiency, and commercial output per GPU node.
• Partner with sales and business development teams during customer technical discovery calls and solution proposals.
• Translate client AI requirements into technical architecture blueprints, Bills of Materials (BOMs), RFPs/RFQs, and execution roadmaps.
• Deliver technical presentations, platform demonstrations, PoCs, and technical onboarding for strategic clients.
• Define project technical boundaries, deliverables, SLAs, and customer acceptance criteria.
• Manage technical delivery with server OEMs, data centre operators, network/storage vendors, and system integrators.
• Oversee hardware delivery, rack mounting, cabling, environment initialization, cluster commissioning, and acceptance.
• Manage project timelines, mitigate technical risks, and drive issue resolution.
• Standardize technical documentation, operational runbooks, and delivery guidelines.
• Build and lead a team of infrastructure and platform engineers as the business scales.

Job Requirements

• Bachelor’s degree or above in Computer Science, Electronic Engineering, Telecommunications, Software Engineering, or a related discipline.
• A minimum of 5 years of experience in Cloud Computing, High-Performance Computing (HPC), AI Infrastructure, or Data Centre Engineering.
• At least 2 years of hands‑on experience with GPU clusters, AI cloud platforms, or large‑scale compute environments.
• Strong proficiency in Linux, Docker, Kubernetes, Helm, and cloud‑native container ecosystems.
• In‑depth knowledge of GPU hardware architectures, parallel compute libraries, driver stacks, and AI server architectures.
• Hands‑on experience with Kubernetes GPU scheduling or Slurm cluster management.
• Solid understanding of high‑speed interconnect technologies, including InfiniBand, RoCE, RDMA, and NVLink.
• Practical understanding of AI workload execution spanning LLM training, fine‑tuning, and inference serving.
• Strong client‑facing communication, cross‑functional collaboration, and vendor management skills.
• Fluent in both English and Mandarin, with the ability to conduct technical meetings and produce technical documentation in both languages.
• Based in Malaysia with willingness to travel within the Southeast Asia region in future.