Join Deloitte's Technology & Transformation practice, a global leader in harnessing data's potential. We empower companies to manage vast datasets from diverse sources, transforming them into actionable insights for informed decision-making. Our expertise spans business intelligence, data management, performance analytics, and cutting-edge technologies like big data, cloud, cognitive computing, and machine learning.

This role focuses on Hybrid Cloud Engineering within our T&T EAD team, offering a unique opportunity to shape the future of AI infrastructure.

Key responsibilities include designing robust GPU landing zones encompassing accounts, network topology, and security. You will select optimal NVIDIA GPU instances and cluster patterns for various AI workloads, from distributed training to low-latency inference. Furthermore, you'll engineer sophisticated cloud GPU clusters leveraging Kubernetes or HPC schedulers, alongside designing high-throughput storage and data movement solutions.

This role demands a strategic approach to infrastructure design and implementation within a hybrid cloud environment.

We seek a seasoned professional with 11-15 years of experience in infrastructure, cloud, SRE, HPC, or platform engineering. Essential expertise includes NVIDIA GPU infrastructure, AI/GPU platforms, and GPU cluster architecture. A deep understanding of distributed AI workloads, GPU scheduling, and high-performance networking is crucial.

Proficiency in Kubernetes/OpenShift or Slurm, fundamental Linux and container technologies, and infrastructure automation with Terraform are required. Demonstrable experience with CI/CD/GitOps practices and deep knowledge of at least one hyperscale cloud platform (AWS, Azure, or GCP) are vital for success in this role.