Position Details
About this role
This role involves designing and validating large-scale AI training and inference architectures on AMD GPUs, focusing on Kubernetes-based solutions for enterprise AI deployments.
Key Responsibilities
- Design reference architectures
- Validate Kubernetes training stacks
- Implement GPU placement strategies
- Collaborate with customers
- Benchmark inference frameworks
Technical Overview
Focuses on GPU-accelerated computing, Kubernetes orchestration, distributed training, inference frameworks like vLLM and SGLang, and optimizing large language model workloads.
Ideal Candidate
The ideal candidate is a solution-oriented AI infrastructure engineer with hands-on experience in GPU-accelerated computing, Kubernetes, and large-scale AI deployments. They should be capable of designing production-ready systems and optimizing AI workloads.
Must-Have Skills
Nice-to-Have Skills
Tools & Platforms
Required Skills
Hard Skills
Soft Skills
Industry & Role
Keywords for Your Resume
Deal Breakers
Lack of experience with GPU-accelerated computing, No knowledge of Kubernetes or distributed training, Inability to develop production AI solutions, No experience with inference frameworks
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