Job
Description
Role Overview: As an AI Infrastructure engineer and project manager at Lenovo, your primary responsibility will be to install, configure, and deploy AI-based Proof of Concepts (PoCs) into full production environments for enterprise customers. You will focus on deploying Nvidia AI Enterprise solutions on Lenovo hardware to ensure seamless, scalable, and robust production implementations, ultimately helping customers unlock the full potential of AI technologies. Key Responsibilities: - Manage the AI production deployment process from testing to final deployment, configuring, installing, and validating AI systems using key platforms such as VMware ESXi, Linux, Windows Server, Docker, and Kubernetes. - Conduct comprehensive performance benchmarking and AI inferencing tests to validate system performance in production and optimize deployed AI models for accuracy, performance, and scalability. - Serve as the primary technical lead for AI POC deployment, working hands-on with Nvidia AI Enterprise and GPU-accelerated workloads, and leading technical optimizations for resource efficiency. - Assume complete ownership of AI project deployments, overseeing all phases from planning to final deployment, collaborating with stakeholders, and implementing risk management strategies. - Develop detailed documentation for each deployment, conduct knowledge transfer sessions, and provide training on the operation, management, and scaling of AI systems. Qualifications: - Educational Background: Bachelors or Masters degree in Computer Science, Engineering, or a related field, or equivalent practical experience in AI infrastructure deployment. - Experience: Minimum 5+ years of experience in deploying AI/ML models using Nvidia GPUs in enterprise production environments, with demonstrated success in leading complex AI infrastructure projects. - Technical Expertise: Extensive experience with Nvidia AI Enterprise, GPU-accelerated workloads, AI/ML frameworks such as PyTorch and TensorFlow, deploying AI solutions across enterprise platforms, and MLOps proficiency with tools like Kubeflow, MLflow, or AWS SageMaker.,
