Nvidia AI Architect- Delivery

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*Key Responsibilities*
:

• 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.
• Ensure the readiness of customer environments to handle, maintain, and scale AI solutions post-deployment.
• take ownership of AI project deployments, overseeing all phases from planning to final deployment, ensuring that timelines and deliverables are met.
• Collaborate with stakeholders, including cross-functional teams (e.g., Lenovo AI Application, solution architects), customers, and internal resources to coordinate deployments and deliver results on schedule.
• 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.

*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), 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.

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.
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*Key Responsibilities*
:

• 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.
• Ensure the readiness of customer environments to handle, maintain, and scale AI solutions post-deployment.
• take ownership of AI project deployments, overseeing all phases from planning to final deployment, ensuring that timelines and deliverables are met.
• Collaborate with stakeholders, including cross-functional teams (e.g., Lenovo AI Application, solution architects), customers, and internal resources to coordinate deployments and deliver results on schedule.
• 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.

*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), 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.

We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, religion, sexual orientation, gender identity, national origin, status as a veteran, and basis of disability or any federal, state, or local protected class.