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What you will be doing:
Lead initiatives to transform IT Compute platform architecture to build new service offerings across On-Prem & Cloud.
Define and implement metrics to measure the efficiency of compute platforms & services and drive efficiency.
Collect and review system data for capacity and planning purposes, analyze capacity data and develop plans for appropriate level enterprise-wide systems, and coordinate with management personnel in implementing changes.
Develop and maintain tools for collecting, analyzing, and visualizing data for reporting, alerting, monitoring.
Collaborate with NVIDIA leadership, senior engineers, program managers, and product managers to develop compelling IT products and services that meet customer needs.
What we need to see:
Bachelor’s degree in Engineering, Computer Science, Mathematics, or related field, or equivalent experience
12+ years of proven experience in compute platform engineering with a focus on automation.
Experience with design and deployment of virtualization architectures, including VMware, Openshift or KubeVirt platforms.
Proven experience evaluating existing application architectures and identify opportunities for containerization to improve scalability, reliability, and efficiency.
Strong analytical skills with the ability to define and track key performance metrics.
Experience in developing tools for data analysis and performance profiling, Development with Terraform, Config Management tools.
Proficiency in programming languages such as Go and/or Python.
Experience with running large environments consisting of BareMetal, large scale virtualized environment with a mix of tens of thousands of VM’s and cloud infrastructure.
Ways to stand out from the crowd:
Deep understanding of other infrastructure components like Storage, DNS, AD, Security Tools etc..
Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
Solid understanding of microservices architecture, infrastructure as code (IaC) and configuration management tools.
Understanding of AI ops and how to leverage LLMs to automate various optimization initiatives
You will also be eligible for equity and .
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