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.