AI Data Center DC Block Architect / AIデータセンター・DCブロックアーキテクト

必須

• Extensive experience in designing, developing, and optimizing modular, pre-engineered data center DC blocks, with a focus on AI and machine learning workloads.
• Deep understanding of the latest hardware and software technologies for AI and machine learning, including specialized accelerators and optimized software stacks.
• Proficiency in cloud infrastructure as code (IaC) tools, such as Terraform, CloudFormation, or Ansible, for managing the DC block infrastructure.
• Strong expertise in container orchestration platforms (e.g., Kubernetes) and CI/CD pipelines for the AI software stack.
• Familiarity with leading AI/ML frameworks, data processing tools, and observability solutions.
• Understanding of cloud security best practices, compliance frameworks, and data protection regulations.
• Excellent problem-solving skills and the
• Available to work at our Lab on a daily basis

求める人物像

• Proactively take on new challenges and approach difficult tasks with a positive mindset
• Excel at coordinating and communicating effectively with both internal and external stakeholders
• Embrace change and adapt flexibly in dynamic, fast-paced environments
• Take initiative to identify and address issues independently
• Possess strong communication skills, both verbal and written
• Demonstrate a willingness to actively learn and grow in unfamiliar areas

使用ツール

About the role

• We are seeking an experienced AI Data Center DC Block Architect to join our team. In this role, you will be responsible for designing, developing, and optimizing modular, pre-engineered DC blocks that are tailored to support our organization's growing AI and machine learning workloads.

Job Scope

• 1. AI Workload Analysis and Requirements:
• Assess the organization's current and future AI and machine learning requirements, including compute, storage, and networking needs.
• Collaborate with data science and IT teams to understand the specific performance, scalability, and reliability requirements of the AI workloads.
• Identify any unique hardware or software considerations for the AI DC blocks, such as the need for specialized accelerators or optimized software stacks.
• 2. DC Block Architecture and Design:
• Design modular, pre-engineered DC blocks that can efficiently support a variety of AI and machine learning workloads.
• Ensure the DC block architecture is scalable, resilient, and aligned with industry best practices and the organization's overall data center strategy.
• Optimize the DC block layout, power, cooling, and infrastructure to maximize performance, energy efficiency, and density.
• 3. Hardware Selection and Integration:
• Evaluate and select the appropriate server hardware, including CPUs, GPUs, and specialized AI accelerators (e.g., NVIDIA Tensor Core GPUs, Google TPUs).
• Determine the optimal storage solutions, considering factors like capacity, performance, and data redundancy (e.g., high-performance SSDs, NVMe, network-attached storage).
• Integrate the networking infrastructure to support the required bandwidth, low-latency communication, and data transfer requirements of the AI workloads.
• 4. Software Stack Development and Optimization:
• Design and develop the software stack for the AI DC blocks, including the operating system, containerization platform, and orchestration tools.
• Integrate and configure leading AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Keras) to enable efficient model development and deployment.
• Implement data management and processing pipelines, leveraging tools like Apache Spark, Hadoop, or custom data ingestion and preprocessing workflows.
• Optimize the software stack for performance, scalability, and resource utilization to ensure the AI DC blocks operate at peak efficiency.
• 5. Monitoring and Observability:
• Develop comprehensive monitoring and observability capabilities for the AI DC blocks, including metrics, logging, and tracing.
• Implement data-driven insights and analytics to identify performance bottlenecks, optimize resource allocation, and ensure overall system reliability.
• Automate deployment, scaling, and management processes to streamline the operation and maintenance of the AI DC blocks.
• 6. Security and Compliance:
• Incorporate robust security measures, such as access controls, network segmentation, and data encryption, into the AI DC block design.
• Ensure compliance with relevant data privacy and regulatory requirements (e.g., GDPR, HIPAA) by implementing appropriate data governance and access policies.
• Develop and test disaster recovery and business continuity plans to ensure the resilience of the AI DC blocks in the event of failures or disasters.
• 7. Continuous Optimization and Scalability:
• Continuously monitor the performance and resource utilization of the AI DC blocks to identify opportunities for optimization.
• Implement auto-scaling and dynamic resource allocation mechanisms to handle fluctuations in AI workload demands.
• Explore options for distributed or federated learning architectures to scale the AI capabilities across multiple edge devices or smaller data centers.

Internal common IT tools

• Google Workspace （Gmail, G-cal, Gmeet等）
• Slack
• Notion
• SmartHR
• Money Forward
• Bakuraku etc.

About Engineering and Research Division

• Our Engineering and Research Division consists of mainly three teams that handle end-to-end development of Hardware and software systems for Energy storage & power transfer solutions and services. Currently, approximately 50 specialists are engaged in the mission of advancing energy storage technologies and solutions.
• The Division is organized into the following teams:
• Series Development: Responsible for prototyping, testing & validation , requirements engineering , series handover of new products including product support and commissioning.
• Advanced Engineering: Responsible for development and experimentation into emerging technologies to sustain our current and future roadmap of energy solutions with focus on a areas viz. embedded development, PCB design, model based development, battery management, power conversion, digital twins, edge computing, cloud solutions ,AI/ML based dispatch optimization, generation and forecasts.
• Product Lifecycle Management: Manages product & project life cycles by tracking across quality gates through development, sourcing, value engineering leading up to manufacturing and after sales activities through cross functional coordination and data intensive product life cycle assessment