Senior Solutions Architect, Generative AI Deployment, AIOps

🕒 April 6

🏄 California, Illinois, +2 more states – Remote

🏄 California – Remote 🌽 Illinois – Remote 🗽 New York – Remote 🍂 Massachusetts – Remote

💵 $184k - $287.5k / year

⏰ Full Time

🟠 Senior

💻 Solutions Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

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

• Partnering with other solution architects, engineering, product and business teams • Understanding their strategies and technical needs and helping define high-value solutions • Dynamically engaging with developers, scientific researchers, and data scientists, gaining experience across a range of technical areas • Strategically partnering with lighthouse customers and industry-specific solution partners targeting our computing platform • Working closely with customers to help them adopt and build creative solutions using NVIDIA technology and MLOps solutions • Analyzing performance and power efficiency of AI inference workloads on Kubernetes • Some travel to conferences and customers may be required (20%)

🎯 Requirements

• BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering or related fields (or equivalent experience) • 8+ years of hands-on experience with Deep Learning frameworks such as PyTorch and TensorFlow • Strong fundamentals in programming, optimizations, and software design, especially in Python • Proficiency in problem-solving and debugging skills in GPU orchestration and Multi-Instance GPU (MIG) management within Kubernetes environments • Experience with containerization and orchestration technologies, monitoring, and observability solutions for AI deployments • Excellent knowledge of the theory and practice of LLM and DL inference • Excellent presentation, communication and collaboration skills

🏖️ Benefits

• equity • benefits