Senior AI Infrastructure Engineer
# Senior AI Infrastructure Engineer **IO Tech Solutions Limited** · China · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[AI-Infrastructure-Engineer]` `[ML-Infrastructure-Engineer]` `[Platform-Engineer]` `[Site-Reliability-Engineer]` `[DevOps-Engineer]` `[Senior-AI-Infrastructure-Engineer]` `[AI-ML-Infrastructure-Engineer]` `[Senior-AI-Platform-Engineer]` `[AI-Infrastructure-Lead]` `[ML-AI-Infrastructure-Engineer]` `[AI-Infrastructure-Architect]` --- ### About the Role Responsibilities 1. Full-Stack AI Infrastructure Architecture & Development: - Build a full-stack AI infrastructure system for quantitative scenarios based on Kubernetes, unifying the management of heterogeneous computing resources (e.g., GPU pooling). - Integrate high-performance communication layers (e.g., RDMA) and drive the unified development of AI training/inference platforms and GPU operation/maintenance platforms. - Streamline the end-to-end workflow from resource scheduling to model deployment, enhancing system efficiency and stability. 2. Intelligent Computing Power Scheduling System Design: - Design a global scheduling mechanism supporting multi-task types and priority strategies, leveraging Volcano scheduler capabilities. - Lead the customization and maintenance of Volcano and core Operators, optimizing elastic scaling and resource utilization based on dynamic demands of quantitative tasks. 3. Hardware-Software Co-Optimization & System Reliability: - Develop an intermediate layer bridging underlying hardware (GPU/networking/storage) and AI frameworks (PyTorch/TensorFlow). - Build GPU elastic resource pools, fault self-healing mechanisms, and unified observability platforms (e.g., monitoring dashboards). - Ensure high-efficiency iteration and high availability of large-scale model training through performance tuning and automated operations. 4. Technical Foresight & Architecture Evolution: - Drive long-term AI Infra roadmap planning, anticipating quantitative business needs in computing scale, training efficiency, and cost control. - Explore and validate cutting-edge architectures (e.g., heterogeneous computing fusion, compute-storage separation, Serverless AI) to enhance infrastructure capabilities and technical barriers. *Qualifications* 1. Bachelors/Masters in Computer Science or related fields, 510 years of experience, with strong self-motivation and execution ability to identify and resolve technical bottlenecks. 2. Deep expertise in AI infrastructure: Kubernetes, GPU resource management, RDMA/high-performance networking, and large-scale distributed AI system design/deployment. 3. Proficient in *Golang/Python* with solid system programming and automation skills. Priority given to candidates with experience in *Volcano/Kueue schedulers, K8s Operator development, or open-source contributions*. 4. Familiar with core resource scheduling principles, GPU lifecycle management (allocation, isolation, elasticity, fault tolerance), and designing high-availability, low-latency strategies for quantitative tasks. 5. Knowledge of mainstream AI frameworks (PyTorch/TensorFlow), with experience in training/inference performance optimization and cross-team collaboration for framework-infra co-optimization. 6. *Preferred: Experience in **FinTech/quantitative AI infrastructure*, understanding of business-critical computing demands, and ability to drive cross-team collaboration and value delivery. Originally posted on Himalayas
- AI-Infrastructure-Engineer
- ML-Infrastructure-Engineer
- Platform-Engineer
- Site-Reliability-Engineer
- DevOps-Engineer
- Senior-AI-Infrastructure-Engineer
- AI-ML-Infrastructure-Engineer
- Senior-AI-Platform-Engineer
- AI-Infrastructure-Lead
- ML-AI-Infrastructure-Engineer
- AI-Infrastructure-Architect
Наблюдалась 2026-10-11, впервые 2026-10-11, источник — Himalayas (JSON API).