5+ years of experience in Cloud AI, LLM deployment, or AI infrastructure.
Deep understanding of Transformer architectures, LLMs, and MoE models.
Strong experience in graph optimization and model compilation workflows.
Strong experience in model accuracy and performance optimization.
Strong experience with one or more AI frameworks, including PyTorch, ONNX, TensorFlow, or JAX.
Experience with AI serving frameworks such as vLLM, SGLang, TGI, TensorRT-LLM, or LMDeploy.
Preferred Qualifications
Familiarity with AI accelerator platforms and large-scale inference systems.
Experience with Quantization, KV-Cache optimization, speculative decoding, continuous batching, Prefix Caching, Parallelism Strategies and distributed inference.
Experience with performance modeling and analysis methodologies, including Roofline analysis, bottleneck identification across software and hardware stacks.
Familiarity with open-source inference frameworks such as llama.cpp, Ollama, or MLC-LLM.
Experience with Kubernetes, Docker, and cloud-native AI infrastructure.
Minimum Qualifications
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.
- OR
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
- OR
- PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
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