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, including Large Language Models (LLMs), agent-based systems, and Retrieval-Augmented Generation (RAG). Practical expertise in training and optimizing neural networks on high-performance (GPU-enabled
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manages model onboarding, platform health, monitoring, and configuration across on-premises GPU systems and cloud providers, ensuring reliability and security. Additionally, the AI Solutions Engineer II
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inference Develop distributed model training and inference architectures leveraging GPU-based compute resources Implement server-less and containerized solutions using Docker, Kubernetes, and cloud-native
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optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a
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://shimadzuinstitute.org/ Faculty members have excellent access to computational resources, including Texas Advanced Computing Center (TACC), and multiple HPCs on campus, including some GPU-heavy clusters as
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environment. Expand your technical expertise while supporting research projects across multiple academic fields, applying advanced techniques such as large-scale data processing and GPU-accelerated computing
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, and interacting with pilots and passengers. Operates and becomes familiar with ground support equipment, such as the aircraft tug, ground power unit (GPU), lavatory service cart, de-icing cart, forklift
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computing (HPC) systems, including GPUs, and programming, such as using CUDA, MPI, AI/ML/DL, and advanced debuggers and performance analyzers. Familiarity with working on open-source projects. About UF
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render diferenciable (NVDiff) -Conocimientos de render de fibras (pelo, ropa) Conocimiento de lenguajes gráficos y GPU: Vulkan, GLSL, CUDA Additional Information Selection process The selection
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). Experience managing systems utilizing GPU (NVIDIA and AMD) clusters for AI/ML and/or image processing. Knowledge of networking fundamentals including TCP/IP, traffic analysis, common protocols, and network