Solutions Architect, CSP GTM

This is a fully remote position, open to applicants in Germany.

📋 Description

• Design and showcase Physical AI solutions utilizing technologies from Hyperscalers and NVIDIA’s software and hardware.

• Collaborate closely with key customers and Hyperscaler partners to identify challenges and deliver solutions utilizing NVIDIA products.

• Conduct thorough analysis and optimization for GPU-accelerated systems leveraging NVIDIA software platforms.

• Aid in the optimization of training and inference pipelines.

• Collaborate with Engineering, Product, and Sales teams to comprehend developer challenges and devise appropriate solutions.

• Facilitate product feature development and expansion through customer insights and proof-of-concept assessments.

• Cultivate industry knowledge and assist in integrating NVIDIA technology into Enterprise Computing frameworks.

• Foster relationships with technical executives and managers to promote accelerated computing and Generative AI.

• Interact with developers, researchers, data scientists, IT managers, and senior leadership.

⛳️ Requirements

• MS/PhD or equivalent experience in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, or other engineering disciplines.

• Over 5 years of experience in Solutions Architecture/Engineering within AI-related fields.

• Strong listening skills, with exceptional verbal and written communication abilities, and comfort presenting technical solutions in English.

• Proficiency in Physical AI solutions on Hyperscaler infrastructure.

• Demonstrated academic and/or industry experience in machine learning, deep learning, and/or data science.

• Capability to collaborate with various levels and teams across Engineering, Product, Sales, and Marketing.

• Self-motivated individual with a desire for growth and a passion for continuous learning and sharing insights with the team.

• Experience with Physical AI solutions and services on Hyperscaler platforms.

• Proficiency in managing and optimizing large-scale distributed deep learning training and refining inference pipelines.

• Knowledge of inferencing techniques, including model compression, model compilation, or model serving.

• Familiarity with larger transformer-based architectures.

• Expertise in DevOps technologies such as Docker, Kubernetes, Singularity, etc.

🏝️ Benefits

• Opportunity to work with cutting-edge technologies in the AI space.

• Collaborate with a diverse team of professionals across multiple disciplines.

• Engage in continuous learning and professional development opportunities.

• Contribute to innovative projects that have a significant impact on the industry.