13 часов назад
Principal Solutions Architect, AI Data Infrastructure
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Principal Solutions Architect, AI Data Infrastructure (AI infrastructure): Designing and optimising end-to-end data paths for AI Factory and Firmus AI Cloud, with an accent on high-performance storage, memory fabrics, caching, and GPU utilisation. Focus on workload-driven benchmarking, integrating storage and memory into Kubernetes and bare-metal GPU clusters, and translating performance, power, capacity, and TCO trade-offs into architecture decisions.
Location: Singapore
Company
Technologies develops and operates energy-efficient AI infrastructure and the AI Cloud platform across Asia Pacific.
What you will do
- Own the end-to-end reference architecture for AI data paths, including parallel and software-defined storage, NVMe/NVMe-oF, object and file systems, memory disaggregation, pooling, KV-cache, and data movement.
- Evaluate and benchmark technologies against real AI workloads, converting results into decisions on performance, capacity, power efficiency, and total cost of ownership.
- Advise customers by sizing solutions, validating workload requirements, and resolving production performance issues.
- Shape the AI Cloud platform roadmap and integrate storage and memory into Kubernetes-based and bare-metal environments.
- Design low-latency connectivity across storage, memory, and GPUs using RDMA, RoCEv2, and InfiniBand.
- Establish operational practices for data protection, resilience, lifecycle management, and large-scale cloud operations.
Requirements
- 10+ years of experience in storage, memory, HPC, or AI infrastructure roles.
- Deep vendor-agnostic expertise in high-performance storage and memory platforms.
- Strong knowledge of NVMe, NVMe-oF, parallel and software-defined storage, object and file systems, RDMA, RoCEv2, and InfiniBand.
- Experience with CXL, memory disaggregation, and KV-cache optimisation for LLM workloads.
- Ability to design workload-driven benchmarks across bare-metal, virtualised, and containerised environments and build architecture and business cases from the results.
- Experience integrating storage and memory into GPU clusters such as DGX/HGX, advising executive customers, writing reference architectures or whitepapers, and mentoring engineers.
Culture & Benefits
- Full-time employment.
- Work focused on sustainable, energy-efficient AI infrastructure.
- Collaboration across engineering teams, compute and networking specialists, customers, and executive stakeholders.
- Inclusive workplace that welcomes candidates from diverse backgrounds.
Будьте осторожны: если работодатель просит войти в их систему, используя iCloud/Google, прислать код/пароль, запустить код/ПО, не делайте этого - это мошенники. Обязательно жмите "Пожаловаться" или пишите в поддержку. Подробнее в гайде →