Ai jobs in United States

We are seeking a Singtel’s GPU-as-a-Service (GPUaaS) Solutions Architect to assist in designing and implementing scalable and secure solutions that align with business objectives and technology standards.

• Analyze and translate business requirements into comprehensive technical solutions that align with Singtel’s GPU-as-a-Service (GPUaaS).
• Design and implement architecture Singtel’s GPU-as-a-Service (GPUaaS) based on business needs and industry best practices.
• Lead technical workshops and discussions to align architecture vision with business goals.
• Co‑design and develop applications/workload architecture with customers and partners and facilitate customer POC, trial and production requirement.
• Collaborate with business stakeholders, product managers, and technical teams to align on solution objectives and priorities.
• Manage relationships and expectations with key stakeholders and clients in the AI domain.
• Ensure architecture solutions are optimized for performance and scalability in GPU cloud environments and guide best practice architecture for Singtel’s GPU-as-a‑Service (GPUaaS).
• Ensure compliance with security, performance, and regulatory standards throughout the solution lifecycle.
• Lead on the planning and execution of the customer workload and data migration on our GPU platform.
• This role may require availability outside standard work hours, including nights, weekends and public holidays.

• Degree in Computer Science, Information Systems, Engineering, or a related field with 8-10 years experiences.
• Strong hand‑on experience in Linux, hypervisor, storage (NFS, Object), and infrastructure as a code.
• Understanding of cloud architectures (IaaS, PaaS), GPU system architecture and NVIDIA GPUs.
• A background in systems level thinking and design. Combined with the ability to translate technical strategy and architectures into concrete, minimal viable products.
• Experience architecting, designing, and developing complex, enterprise grade, configurable, scalable and high‑performance architectures across GPU cloud platform for AI solution.
• Strong problem‑solving skills and the ability to handle complex technical challenges.
• Exposure to software development would be an added advantage.
• Deep understanding of the architectural principles for cloud‑based platforms that include SaaS, PaaS, multi‑tenancy, infrastructure as code, and continuous availability with hand‑on.
• Excellent verbal, written, and presentation skills in English.
• Experience in stakeholder management and client engagement, ability to collaborate effectively across various cross‑functional teams and groups.

• Understanding how AI and HPC workloads interact with both GPU HW and SW infrastructure.
• Knowledge with orchestration platforms like Kubernetes, SLURM using ML frameworks.
• System‑level experience specifically GPU‑based systems.
• Understanding of how collective communications (MPI, RDMA, and NCCL) works on GPU cluster, as well as an understanding of GPU specific acceleration works.
• Understanding of AI & HPC networking technologies such as InfiniBand, RoCE, DPUs.