Description
NVIDIA is looking for a Senior Cloud Infrastructure Solutions Architect to join its ANZ Team, specifically focused on working with Neo Clouds.
As a Senior Cloud Infrastructure Solutions Architect, you will be involved with the crew developing many of the largest and fastest AI/HPC systems in the world. You will work on a dynamic customer-focused team that requires excellent interpersonal skills. Your role will involve interacting with Neo Clouds, customers, partners, and various departments to analyze, define, and implement large-scale AI Operational projects.
Responsibilities
- Maintaining large-scale computational and AI infrastructure, focusing on monitoring, logging, workload orchestration (Kubernetes and Linux job schedulers).
- Optimizing scalable, production-ready Kubernetes-based container platforms coordinated with enterprise-grade networking and storage.
- Serving as a key technical resource, developing, refining, and documenting standard methodologies and operational guidelines to be shared with internal teams.
- Leading technical engagements, helping customers troubleshoot, resolve complex issues, and advocate for their technical needs.
- Supporting Enterprise, Research & Development activities and engaging in POCs/POVs to validate new features, architectures, and upgrade approaches.
- Partnering with APAC Regional and HQ Engineering, Product, and Sales teams to develop, plan best suitable solutions for Partners.
- Developing tooling to automate deployment and management of large-scale infrastructure environments, to automate operational monitoring and alerting, and to enable self-service consumption of resources.
- Creating and delivering high-quality documentation, including runbooks, onboarding materials, and best-practice guides for customers and internal teams.
- Becoming the technical leader for assigned customer accounts, providing strategic guidance on DevOps and platform architecture and influencing long-term infrastructure and operations decisions.
Requirements
- Education & Experience: BS/MS/PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related fields, with 5+ years of professional experience in managing scalable cloud environments and automation engineering roles.
- Proven Hardware & Software knowledge and experience at scale: Familiarity with HPC and AI technologies (CPUs, GPUs, high-speed interconnects) and supporting software stacks.
- Cloud & HPC Expertise: Proven understanding of networking fundamentals (TCP/IP stack), data center architectures (from storage to Network, GPU accelerated compute and of course DevOps software stacks, as well as hands-on experience managing HPC/AI clusters, including deployment, optimization, and fixing issues.
- Kubernetes & AI/ML Workloads: Extensive experience with Kubernetes for container orchestration, resource scheduling, scaling, and integration with HPC environments.
- Linux & Storage Systems: Deep knowledge of Linux (RedHat/CentOS, Ubuntu), OS-level security, and protocols (TCP, DHCP, DNS). Experience with storage solutions such as Lustre, GPFS, ZFS, XFS, and emerging Kubernetes storage technologies.
- Automation & Observability: Proficiency in Python and Bash scripting, configuration management, and Infrastructure-as-Code tools (e.g., Ansible, Terraform). Experience with observability stacks (Grafana, Loki, Prometheus) for monitoring, logging, and building fault-tolerant systems.
- Solution Architecture & Customer Engagement: Strong background in crafting scalable solutions and providing consultative support to customers.
Nice to Have
- Experience with GPU-focused hardware and software (e.g., NVIDIA DGX, CUDA, GPU Operator).
- Solid hands-on knowledge of Kubernetes and container-based microservices architectures.
- Skills in large-scale computing and cluster computing, data center design include high-speed interconnect with Spectrum/InfiniBand, Cluster Storage and Scheduling related design and/or management experience.
- Knowledge of CI/CD pipelines for software deployment and automation.
- Background with RDMA-based fabrics (InfiniBand or RoCE) in HPC or AI environments.