Position Details
About this role
This role involves leading the development and optimization of enterprise GPU platforms for AI workloads, focusing on NVIDIA GPU ecosystems, large language models, and multi-cloud deployment strategies.
Key Responsibilities
- Design GPU architectures
- Lead AI platform strategy
- Optimize GPU workloads
- Implement high-performance inference pipelines
- Manage multi-cloud GPU deployments
Technical Overview
The position requires deep expertise in NVIDIA GPU hardware, CUDA, LLM/SLM runtimes like Triton and vLLM, GPU orchestration, and performance tuning for high-performance AI inference systems across hybrid cloud environments.
Ideal Candidate
The ideal candidate is a lead engineer with over 7 years of experience in AI infrastructure, specializing in NVIDIA GPU ecosystems, LLM/SLM runtimes, and GPU orchestration. They possess strong expertise in performance tuning, model quantization, and managing large-scale AI deployments across multi-cloud environments.
Must-Have Skills
Nice-to-Have Skills
Tools & Platforms
Required Skills
Hard Skills
Soft Skills
Industry & Role
Keywords for Your Resume
Deal Breakers
Less than 7 years of engineering experience, No experience with NVIDIA GPU or CUDA, Lack of experience with LLM/SLM runtimes, No hands-on experience with GPU orchestration, Unable to work in a hybrid environment
Get matched to jobs like this
Luna finds roles that fit your skills and career goals — no endless scrolling required.
Create a Free Profile