Amazon
Senior Delivery Consultant – DevOps, WWPS ProServe
$154K–$208K
Arlington, VA +2 · Senior · Full-time · Posted Sep 8, 2026
- Location
- Arlington, VA
Denver, CO
Herndon, VA - Experience
- 7+ years in technical specialist, design and architecture; 7+ years in customer-facing complex, large-scale project management; 5+ years in object-oriented software development; 5+ years in CI/CD.
- Visa sponsorship
- Must be a US Citizen and currently possess and maintain an active TS/SCI security clearance with polygraph.
About the job
Designs and delivers complex AWS and DevOps platforms for customers, providing technical guidance throughout implementation and project delivery.
What you'll do
- Design, develop, and implement scalable, secure customer solutions; integrate third-party products into unified platform stacks.
- Provide technical guidance and troubleshooting, and advise customers on industry trends and emerging technologies.
- Mentor and train colleagues and create reusable artifacts.
What they ask for
- 7+ years of technical specialist, design and architecture experience.
- 7+ years of external or internal customer-facing, complex and large-scale project management experience.
- 5+ years of software development with an object-oriented language and 5+ years of CI/CD experience.
- Must be a US Citizen and currently possess and maintain an active TS/SCI security clearance with polygraph.
Nice to have
- Experience operating bare-metal Kubernetes platforms at scale in air-gapped or high-security environments; SpectroCloud PaletteAI or similar is strongly preferred.
- Hands-on experience with NVIDIA AI Enterprise, Run:AI, or equivalent GPU orchestration and scheduling platforms for large-scale AI/ML workloads.
Pay
153,600.00 - 207,800.00 USD annually (base salary); 153,600.00 - 207,800.00 USD annually (base salary); 153,600.00 - 207,800.00 USD annually (base salary)
Tools
AWS, Kubernetes, SpectroCloud PaletteAI, NVIDIA AI Enterprise, Run:AI, CI/CD
AI focus
Preferred work includes orchestrating and scheduling GPUs for large-scale AI/ML workloads, including multi-tenant resource management, distributed-training gang scheduling, and GPU fleet health monitoring.
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