DevOps Deployment Automation Setup
Budget: $25 – $50 USD
I’m ready to replace our manual release routine with a fully automated pipeline that covers every product we ship—our web app, the iOS / Android builds, and the APIs that power them. The outcome I’m after is a single, dependable flow that pushes code from commit to production with zero downtime.
What you will be doing:
You will be part of the DGX Cloud team responsible for production systems that enable large scalable GPU clusters to be used for a variety of AI workloads. This includes working on custom software related to GPU asset provisioning, configuration, and lifecycle management across cloud providers.
Implementing monitoring and health management capabilities that enable industry-leading reliability, availability, and scalability of GPU assets. You will be harnessing multiple data streams, ranging from GPU hardware diagnostics to cluster and network telemetry.
Working with teams across NVIDIA to ensure production AI clusters run consistently with maximum performance. Evaluating system failures and improving services based on a well-defined incident management process.
What we need to see:
You possess a BS in Computer Science, Engineering, Physics, Mathematics or a comparable Degree (or equivalent experience)
Direct experience in a DevOps role within a highly technical organization with demonstrable impact from your work.
4 years of direct experience improving reliability, implementing monitoring framework and being part of a production on-call team
4+ years in similar role and experience on large-scale production systems. Experience with the DevOps principles, tools and techniques.
4 years of technical experience with programming language Python.
Highly motivated with strong communication skills, you can work successfully
What you will be doing:
You will be part of the DGX Cloud team responsible for production systems that enable large scalable GPU clusters to be used for a variety of AI workloads. This includes working on custom software related to GPU asset provisioning, configuration, and lifecycle management across cloud providers.
Implementing monitoring and health management capabilities that enable industry-leading reliability, availability, and scalability of GPU assets. You will be harnessing multiple data streams, ranging from GPU hardware diagnostics to cluster and network telemetry.
Working with teams across NVIDIA to ensure production AI clusters run consistently with maximum performance. Evaluating system failures and improving services based on a well-defined incident management process.
What we need to see:
You possess a BS in Computer Science, Engineering, Physics, Mathematics or a comparable Degree (or equivalent experience)
Direct experience in a DevOps role within a highly technical organization with demonstrable impact from your work.
4 years of direct experience improving reliability, implementing monitoring framework and being part of a production on-call team
4+ years in similar role and experience on large-scale production systems. Experience with the DevOps principles, tools and techniques.
4 years of technical experience with programming language Python.
Highly motivated with strong communication skills, you can work successfully