【Engineering & Research Division】AI Data Center Architect / AIデータセンターアーキテクト

必須

• Extensive experience in designing, implementing, and managing medium-sized AI data centers and infrastructure.
• Deep understanding of the latest hardware and software technologies for AI and machine learning workloads.
• Proficiency in cloud infrastructure as code (IaC) tools, such as Terraform, CloudFormation, or Ansible.
• Strong expertise in container orchestration platforms (e.g., Kubernetes) and CI/CD pipelines.
• 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 ability to think strategically
• 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 Architect to join our team. In this role, you will be responsible for designing, implementing, and optimizing a medium-sized AI data center that leverages the latest hardware and software technologies to support our organization's growing AI and machine learning workloads.

Job Scope

• 1. AI Data Center Architecture and Design:
• Assess the organization's current and future AI and machine learning requirements, including compute, storage, and networking needs.
• Design a scalable, resilient, and efficient AI data center architecture that can accommodate a variety of AI workloads, such as model training, inference, and data processing.
• Ensure the architecture aligns with industry best practices, regulatory compliance, and the organization's IT and business strategies.
• 2. Hardware and Infrastructure Selection:
• 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).
• Ensure the networking infrastructure can support the required bandwidth, low-latency communication, and data transfer requirements.
• Incorporate power and cooling considerations, such as efficient cooling systems and redundant power supplies, to maintain optimal operating conditions.
• 3. Software Stack Integration and Optimization:
• Evaluate and integrate the appropriate operating system (e.g., Linux distributions), containerization platform (e.g., Docker, Kubernetes), 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 data center operates at peak efficiency.
• 4. Monitoring, Observability, and Automation:
• Implement comprehensive monitoring and observability tools to track the performance, resource utilization, and health of the AI data center.
• Develop data-driven insights and analytics to identify bottlenecks, optimize resource allocation, and ensure overall system reliability.
• Automate deployment, scaling, and management processes to streamline the operation and maintenance of the AI data center.
• 5. Security and Compliance:
• Implement robust security measures, such as access controls, network segmentation, and data encryption, to protect the AI data center from potential threats.
• 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 data center in the event of failures or disasters.
• 6. Continuous Optimization and Scalability:
• Continuously monitor the AI data center's performance and resource utilization to identify opportunities for optimization.
• Implement auto-scaling and dynamic resource allocation mechanisms to handle fluctuations in workload demands.
• Explore options for distributed or federated learning architectures to scale the AI capabilities across multiple edge devices or smaller data centers.
• 7. Collaboration and Knowledge Sharing:
• Provide technical leadership and mentor junior engineers on AI data center best practices and strategies.
• Collaborate with cross-functional teams (data science, IT operations, security) to ensure the AI data center meets the organization's evolving needs.
• Document and share knowledge, solutions, and lessons learned to promote continuous improvement within the organization.

Internal common IT tools

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