Lenovo

You as AI Infrastructure engineer and project manager will install, configure, and deploy the tested and validated AI-based Proof of Concepts (PoCs) into full production environments for enterprise customers. You will focus on deploying the Nvidia AI Enterprise solutions on Lenovo hardware and ensuring seamless, scalable, and robust production implementations. Your work will help customers unlock the full potential of AI technologies by managing deployments that ensure AI adoption and optimization of AI models in real-world use cases.

Your 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. Use 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 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. Assume an 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 BDMS, solution architects), customers, and internal resources to coordinate deployments and deliver results on schedule.
• Develop and deliver detailed documentation for each deployment, covering installation procedures, system configurations, and validation reports, ensuring operational teams have clear guidance on managing the deployed systems. Provide comprehensive training sessions on the operation, management, and scaling of AI systems, ensuring that customers are fully prepared for ongoing operations post-handoff
• 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 teams on managing AI infrastructure, troubleshooting common issues, and optimizing AI models.

What do you need to succeed in this opportunity:

• Minimum 5+ years of experience in deploying AI/ML models using Nvidia GPUs in enterprise production environments.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience in AI infrastructure deployment is an advantage.
• Demonstrated success in leading and managing complex AI infrastructure projects, including PoC transitions to production at scale.
• Experience with Nvidia AI Enterprise, GPU-accelerated workloads, and AI/ML frameworks such as PyTorch and TensorFlow.
• Good understanding of virtualization and containerization technologies to ensure robust and scalable deployments.
• Proficiency 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.
• Certifications (Not all Reqiured): PMP certification or equivalent project management certification / certifications in Nvidia AI Enterprise, VMware, cloud integration, Lenovo server platforms, machine learning, or data analytics are highly desirable / NVIDIA Certified Solutions Architect or related certification

 * This role requires the ability and willingness to travel up to 50% of the time to collaborate with clients on-site during PoC testing, system deployment, and production rollouts.