Nvidia AI Solution Architect m/f/d

• Lead end-to-end transitions of AI PoCs into production environments, managing the entire process from testing to final deployment.

• Configure, install, and validate AI systems using key platforms, including:
• VMware ESXi and vSphere for server virtualization, Linux (Ubuntu/RHEL) and Windows Server for operating system integration, Docker and Kubernetes for containerization and orchestration of AI workloads.
• Conduct comprehensive performance benchmarking and AI inferencing tests to validate system performance in production.
• Optimize deployed AI models for accuracy, performance, and scalability to ensure they meet production-level requirements and customer expectations.
• Serve as the primary technical lead/SME for the AI POC deployment in enterprise environments, focusing on AI solutions powered by Nvidia GPUs.
• Work hands-on with Nvidia AI Enterprise and GPU-accelerated workloads, ensuring efficient deployment and model performance using frameworks such as PyTorch and TensorFlow.
• Lead technical optimizations aimed at resource efficiency, ensuring that models are deployed effectively within the customer’s infrastructure.
• Implement risk management strategies and develop contingency plans to mitigate potential issues such as hardware failures, network bottlenecks, and software incompatibilities.
• Maintain ongoing, transparent communication with all relevant stakeholders, providing updates on project status and addressing any issues or changes in scope.
• Conduct post-deployment knowledge transfer sessions to educate client/ Lenovo Managed services teams on managing AI infrastructure, troubleshooting common issues, and optimizing AI models.
• Provide comprehensive training sessions on the operation, management, and scaling of AI systems, ensuring that customers are fully prepared for ongoing operations post-handoff.

Experience:

• Overall experience 7-10 years

• Relevant experience of 2-4 years in deploying AI/ML models/ AI solutions using Nvidia GPUs in enterprise production environments.
• Demonstrated success in leading and managing complex AI infrastructure projects, including PoC transitions to production at scale.

Technical Expertise:

• Experience in the area of Retrieval Augmented Generation (RAG), NVIDIA AI Enterprise, NVIDIA Inference Microservices (NIMs), NVIDIA NeMo framework, Model Management, Kubernetes

• Extensive experience with Nvidia AI Enterprise, GPU-accelerated workloads, and AI/ML frameworks such as PyTorch and TensorFlow.
• Proficient in deploying AI solutions across enterprise platforms, including VMware ESXi, Docker, Kubernetes, and Linux (Ubuntu/RHEL) and Windows Server environments.
• MLOps proficiency with hands-on experience using tools such as Kubeflow, MLflow, or AWS SageMaker for managing the AI model lifecycle in production.
• Strong understanding of virtualization and containerization technologies to ensure robust and scalable deployments.