AI Data Center Architect

Engineering & Research Division / AI Data Center Architect

About Engineering & Research Division

The Engineering & Research Division is responsible for the end-to-end development of PowerX's hardware and software systems for energy storage and power transfer solutions.

The division consists of approximately 50 specialists and is organized mainly into the following teams:

Series Development: Responsible for prototyping, testing, validation, requirements engineering, product handover, product support, and commissioning.

Advanced Engineering: Responsible for research and development of emerging technologies, including embedded systems, PCB design, model-based development, battery management, power conversion, digital twins, edge computing, cloud solutions, and AI/ML-based optimization.

Product Lifecycle Management: Responsible for managing product and project lifecycles across quality gates, sourcing, value engineering, manufacturing, and after-sales activities.

You will work in a diverse engineering environment with members from various technical and cultural backgrounds. The team values autonomy, ownership, and hands-on problem solving, and engineers are encouraged to take initiative in shaping technical decisions.

Description

About the role

We are looking for an AI Data Center Architect to join our Engineering & Research Division.

PowerX is exploring and developing next-generation energy infrastructure solutions, including battery energy storage systems, EV charging infrastructure, and AI data center-related technologies. As AI and machine learning workloads continue to grow, data centers require highly optimized infrastructure across compute, storage, networking, power, cooling, software, and operations.

In this role, you will be responsible for designing, implementing, and optimizing AI data center architecture that leverages the latest hardware and software technologies. You will work across infrastructure, energy systems, cloud platforms, AI/ML workloads, and operations to create scalable, resilient, and energy-efficient data center solutions.

This role is not limited to cloud architecture. It requires a strong understanding of how AI workloads translate into physical infrastructure requirements, including GPU/accelerator selection, storage, networking, power distribution, cooling, monitoring, automation, security, and operational scalability.

Job Scope

Design scalable, resilient, and efficient AI data center architecture to support AI/ML workloads such as model training, inference, and data processing

Assess current and future AI workload requirements, including compute, GPU/accelerator, storage, networking, latency, power, and cooling needs

Evaluate and select server hardware, CPUs, GPUs, AI accelerators, storage systems, networking infrastructure, and related data center components

Design infrastructure that supports high-performance, low-latency, and high-density AI workloads

Incorporate power distribution, cooling, redundancy, and energy efficiency considerations into the overall architecture

Evaluate and integrate operating systems, container platforms, orchestration tools, and AI/ML software stacks

Support integration of AI/ML frameworks, data processing pipelines, observability tools, and automation platforms

Develop monitoring and observability capabilities to track system performance, resource utilization, reliability, and bottlenecks

Design automation for deployment, scaling, operation, and maintenance of AI data center infrastructure

Implement security measures, including access control, network segmentation, encryption, identity management, and data protection

Support disaster recovery, business continuity, and operational resilience planning

Collaborate with cross-functional teams, including data science, software engineering, cloud infrastructure, security, electrical engineering, and business teams

Provide technical leadership and document architecture decisions, design principles, lessons learned, and best practices

Requirements

Extensive experience designing, implementing, or managing AI data center infrastructure, cloud infrastructure, high-performance computing infrastructure, or GPU-based compute environments

Deep understanding of infrastructure requirements for AI and machine learning workloads, including compute, GPU/accelerator, storage, networking, latency, scalability, and reliability

Experience selecting or evaluating server hardware, GPUs, storage systems, networking components, or data center infrastructure

Strong understanding of cloud infrastructure, container orchestration, and CI/CD pipelines

Experience with Infrastructure as Code tools such as Terraform, CloudFormation, Ansible, or similar tools

Experience with Kubernetes, Docker, or other container orchestration platforms

Familiarity with AI/ML frameworks, data processing platforms, and observability solutions

Understanding of data center power, cooling, redundancy, and physical infrastructure considerations

Understanding of cloud security best practices, access control, network segmentation, compliance, and data protection

Excellent problem-solving skills and the ability to think strategically across both technical and business requirements

Business-level English communication skills

Preferred Experiences

Experience with AI/HPC infrastructure, GPU clusters, or high-density compute environments

Experience with NVIDIA GPU platforms, CUDA-based workloads, Tensor Core GPUs, or AI accelerator infrastructure

Experience with data center networking, low-latency architecture, RDMA, InfiniBand, Ethernet fabrics, or high-throughput data transfer

Experience with storage technologies such as NVMe, distributed storage, object storage, NAS, or high-performance file systems

Experience with monitoring and observability tools such as Prometheus, Grafana, Datadog, OpenTelemetry, or similar tools

Experience with automation, auto-scaling, resource scheduling, or workload orchestration

Experience with disaster recovery, business continuity planning, and mission-critical infrastructure operations

Experience with energy-efficient data center design, liquid cooling, thermal management, or power optimization

Experience in cloud platforms such as AWS, GCP, Azure, or private cloud environments

Experience working with electrical, mechanical, facilities, or data center operations teams

Japanese communication skills are a plus