Become a trusted advisor to the top customers, helping them understand and incorporate AI accelerators into their overall cloud and IT strategy by designing training and inferencing platforms, using the accelerators Google Cloud has to offer.
Demonstrate how Google Cloud is differentiated, highlighting the power of accelerators by working with customers on POCs, demonstrating features, optimizing model performance, profiling, and bench marking.
Design and implement complex, multi-host AI training and inferencing solutions on Google Cloud TPUs, focusing on scalability and performance tuning.
Conduct performance profiling and optimization of customer models and data pipelines for the TPU architecture, identifying and resolving bottlenecks.
Advise customers on best practices for integrating their MLOps workflows with the Google Cloud AI Platform ecosystem for TPU utilization.
Minimum qualifications:
Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience.
10 years of experience with cloud native architectures and modern cloud infrastructure with networking - switching/routing for ethernet/RoCE/infiniband, in customer-facing or support roles.
Experience developing and deploying models using deep learning frameworks (TensorFlow, PyTorch, or JAX).
Preferred qualifications:
Master's degree in Computer Science, Mathematics, a related technical field.
Experience as an IT infrastructure consultant or enterprise architect working in data center investment strategies and proposals.
Experience with AI Infrastructure systems, networking technologies (e.g., DPU, RoCE, InfiniBand), cooling, and accelerators, GPUs and TPUs.
Experience in leveraging main AI and software stacks and platforms to bring up and deploy AI compute clusters.
Knowledge of the AI infrastructure market, including main technology providers, differentiators and trends.
Ability to work and grow in fluid environments.
