Senior Solutions Architect, Retail

🕒 April 21

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Logo of NVIDIA

NVIDIA

10,000+ employees

Founded 1993

🤖 Artificial Intelligence

🎮 Gaming

Artificial Intelligence • Gaming • Automotive

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Build complex agentic systems featuring multi-agent coordination, long-horizon reasoning, and advanced planning frameworks. • Develop full-scale solutions, including domain-specific enterprise agents and high-performance retrieval pipelines (RAG) spanning various data sources. • Optimize inference performance by bringing to bear GPU-accelerated frameworks and the full NVIDIA AI infrastructure stack. • Build hands-on PoCs and reference architectures that serve as the blueprint for production-grade generative AI pipelines. • Collaborate alongside Enterprise ISVs to integrate NVIDIA software into native platforms, accelerating the deployment of production workloads. • Collaborate with diverse internal teams to improve NVIDIA software through feedback from real-world implementations. • Empower partner engineering teams through technical workshops, deep-dive architecture reviews, and developer enablement. • Scale global expertise by crafting reusable assets and documentation that help field teams deploy agentic AI at scale.

🎯 Requirements

• BS/MS/PhD in Computer Science, Electrical Engineering, AI/ML, or equivalent experience. • 8+ years of experience in deep learning, machine learning, or distributed AI systems. • Strong programming and debugging experience in Python, C/C++, and Linux environments. • Background in using deep learning libraries like PyTorch or TensorFlow. • Hands-on experience building LLM and generative AI applications. • Experience working with agentic or multi-agent AI systems employing frameworks such as: LangGraph, LlamaIndex, CrewAI, LangChain, OpenAI Agents SDK or similar orchestration frameworks. • Experience building tool-using AI agents that interact with APIs, databases, and enterprise systems. • Ability to rapidly prototype AI applications and build scalable GPU-accelerated architectures.

🏖️ Benefits

• equity • benefits

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