Solutions Engineer

Role Overview

• Activate partners to drive adoption of our AI solutions across various industries.
• Help partners identify and build Secure Factory with NVIDIA AI Use Cases for both core and edge.
• Work with partners to develop and deliver Secure AI Factory workshops.
• Play a pivotal role in facilitating the success of partner Field Sales teams in selling the full suite of Cloud and AI infrastructure solutions.
• Help them position data center virtualization and systems architecture solutions effectively against competing offerings.
• Passionate about presales engineering activities, demonstrating solutions and architectures in Cloud and AI infrastructure.
• Build awareness, fostering education and driving energy for AI with both internal and external partner team members.
• Act as a link between Global and APO sales and partner teams.
• Showcase AI solutions/products, its potential and promote its tactical and technical responsibilities.
• Play an important role in the design, development and promotion of AI Solutions with our partners and address challenges.

Requirements

• Significant experience with end-to-end architecture/design work across multiple technology areas for DC and hybrid cloud solutions.
• Public, hybrid and private cloud computing and architecture experience along with Virtualization or X86 Architectures or Operating Systems.
• Experienced in the design and/or deployment of Data Center solutions including traditional DC standalone design, VXLAN fabric-based architectures.
• Experience providing consumable documentation of standard methodologies for deployment around application acceleration, automation/management efficiencies, enterprise edge, and/or AI/ML solutions.
• 6+ years knowledge of any combination of Datacenter, Storage, Compute, Apps, Big Data, Converged Infrastructure, AI infrastructure or Data Center Networking experience.
• Bachelor's Degree or equivalent in Computer Science, Computer Engineering, Electrical Engineering, or related field. Advanced degree is a plus.
• Experience working with partners to build and deliver GTM activities.
• Knowledge and understanding of networking protocols and technologies.
• Excellent presentation skills
• ability to deliver engaging workshops to both technical and non-technical audiences on AI topics.
• AI experience with Nvidia, IBM, Microsoft, Dell, NetApp, HPE, and/or other AI vendors.
• In-depth understanding of language models, including but not limited to GPT-3, BERT, or similar architectures.
• Expertise in training and fine-tuning LLMs using popular frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.
• Experience in deploying LLM models in cloud environments (e.g., AWS, Azure, GCP) and on-premises infrastructure.
• Familiarity with containerization technologies (e.g., Docker or equivalent experience) and orchestration tools (e.g., Kubernetes) for scalable and efficient model deployment.

Tech Stack

• AWS
• Azure
• Cloud
• Docker
• Google Cloud Platform
• Kubernetes
• PyTorch
• Tensorflow