Data Center & Cloud Infrastructure and Service Orchestrator Architect

Location:Japan

ByteBridge is a widely trusted innovator of IT services, including data center, enterprise IT, and unified communications. ByteBridge was founded by a team of passionate dedicated experts with a vision to achieve customer success through technology enablement. We bridge the technical gaps to help enterprises' vision come true and expand their business on a global scale. To date, ByteBridge has been trusted by some of the world's leading international companies.

We are seeking an expert Data Center & Cloud Infrastructure and Service Orchestrator Architect to design and implement the service orchestration layer that will deploy and manage diverse workloads on top of our multi-region cloud infrastructure. This role focuses on creating the intelligent orchestration systems that automate the deployment, scaling, and management of applications, databases, AI/ML services, and other cloud services.

• Service Orchestration Platform Design
  • Design comprehensive service orchestration platforms for automated workload deployment and management
  • Architect API-driven service provisioning systems with self-service capabilities
  • Design multi-tenant service isolation and resource allocation frameworks
  • Create service lifecycle management systems including deployment, scaling, updates, and decommissioning
• Workload Orchestration Architecture
  • Design orchestration systems for diverse workload types:
    • Virtual machine provisioning and management
    • Container orchestration using Kubernetes
    • Database service deployment (SQL, NoSQL, distributed databases)
    • Message queue services (Kafka, RabbitMQ, Apache Pulsar)
    • GPU-accelerated AI/ML services and model inference platforms
    • Large Language Model (LLM) fine-tuning and inference services similar to AWS Bedrock
• AI/ML Service Orchestration
  • Architect AI/ML pipeline orchestration for model training, validation, and deployment
  • Design GPU resource scheduling and allocation systems for distributed training
  • Create model serving infrastructure with auto-scaling and load balancing
  • Design MLOps platforms for continuous integration and deployment of ML models
  • Architect LLM inference services with dynamic scaling and cost optimization
• Service Discovery and Integration
  • Design service mesh architectures for microservices communication
  • Architect API gateway and service proxy solutions
  • Create service discovery, configuration management, and secrets management systems
  • Design inter-service communication patterns and protocols
• Automation and DevOps Integration
  • Design CI/CD pipelines integrated with service orchestration platforms
  • Architect GitOps workflows for declarative service management
  • Create policy-based governance and compliance automation
  • Design cost management and resource optimization automation

• Experience
  • 12+ years in distributed systems and service orchestration
  • 8+ years with container orchestration platforms (Kubernetes, Docker Swarm)
  • 6+ years with cloud-native application architecture and microservices
  • 5+ years with AI/ML infrastructure and model deployment systems
• Service Orchestration Expertise
  • Kubernetes: Expert-level knowledge of Kubernetes architecture, custom resources, operators, and ecosystem tools
  • Container Technologies: Deep understanding of Docker, containerd, container networking, and storage
  • Service Mesh: Experience with Istio, Linkerd, or Consul Connect
  • API Design: Proficiency in REST, GraphQL, and gRPC API design and implementation
  • Workflow Orchestration: Experience with Apache Airflow, Temporal, or similar workflow engines
• AI/ML and Data Services
  • ML Orchestration: Experience with Kubeflow, MLflow, or similar ML pipeline platforms
  • GPU Computing: Knowledge of CUDA, distributed training frameworks (PyTorch, TensorFlow)
  • Model Serving: Experience with model inference platforms (TensorFlow Serving, TorchServe, NVIDIA Triton)
  • LLM Infrastructure: Understanding of large language model deployment, fine-tuning, and inference optimization
  • Data Platforms: Experience with big data orchestration (Apache Spark, Flink, Kafka)
• Cloud-Native Technologies
  • DevOps Tools: Proficiency with Jenkins, GitLab CI, ArgoCD, or similar CI/CD platforms
  • Infrastructure as Code: Experience integrating with Terraform, Pulumi, or CloudFormation
  • Monitoring & Observability: Knowledge of Prometheus, Jaeger, Fluentd, and cloud-native monitoring stacks
  • Security: Understanding of container security, RBAC, network policies, and secrets management
• Programming and Automation
  • Languages: Proficiency in Go, Python, or Java for building orchestration tools and operators
  • Scripting: Advanced skills in Bash, Python for automation and integration scripts
  • Database Technologies: Experience orchestrating various database types (PostgreSQL, MongoDB, Elasticsearch, Redis)
  • Message Queues: Deep knowledge of Kafka ecosystem, RabbitMQ, and event-driven architectures
• Preferred Qualifications
  • Experience building platform-as-a-service (PaaS) or infrastructure-as-a-service (IaaS) offerings
  • Knowledge of serverless computing platforms and function-as-a-service (FaaS)
  • Experience with edge computing and distributed cloud orchestration
  • Certifications: CKA, CKAD, CKS (Kubernetes), or relevant cloud platform certifications
  • Experience with FinOps and cloud cost optimization
• Education & Certifications
  • Bachelor's degree in Computer Science, Software Engineering, or related field
  • Master's degree in distributed systems or related area preferred
  • Industry certifications in Kubernetes, cloud platforms, or DevOps practices
• Key Competencies
  • Strong software engineering and system design skills
  • Deep understanding of distributed systems patterns and microservices architecture
  • Excellent problem-solving abilities for complex orchestration challenges
  • Experience with agile development practices and cross-functional team collaboration
  • Strong communication skills for technical architecture discussions
• Work Environment
  • Collaborative environment working closely with infrastructure and application teams
  • Opportunity to work with cutting-edge AI/ML and cloud-native technologies
  • Fast-paced environment with focus on automation and self-service capabilities
  • Regular interaction with customer technical teams and requirements gathering