Proficient in Programming languages such as Python. Familiar with AI/ML concepts, algorithms, and techniques, with hands-on experience in delivering AI/ML solutions. Practical knowledge of infrastructure components including compute, networking, and storage for modern cloud environments, especially with Kubernetes. Solid understanding of the AI/ML development lifecycle, with familiarity in MLOps practices such as model deployment, serving, and monitoring. Experience with LLM inferencing frameworks (vLLM, SGLang, TensorRT-LLM) and LLM model optimization (nice to have) Experience with ML model serving and orchestration frameworks such as MLflow, Seldon, Triton Inference Server, or Ray Serve (nice to have).
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