Delivery Consultant - DevOps, WWPS ProServe

About the role

The Amazon Web Services Professional Services (ProServe) team is seeking a skilled Delivery Consultant to join our team at Amazon Web Services (AWS).

This role involves working closely with customers to design, implement, and manage AWS solutions that meet their technical requirements and business objectives.

Responsibilities

• Designing, developing, and implementing complex, scalable, and secure solutions tailored to customer needs
• Providing technical guidance and troubleshooting support throughout project delivery
• Integrate multiple third-party software products into a unified platform stack
• Act as a trusted advisor to customers on industry trends and emerging technologies
• Leading the implementation process, ensuring adherence to best practices, optimizing performance, and managing risks throughout the project

Requirements

• 3+ years of programming in Python, Ruby, Go, Swift, Java, .Net, C++ or similar object oriented language experience
• Experience with CloudFormation, Chef, Puppet, Salt, or Ansible in production environments
• 3+ years of cloud architecture and solution implementation experience
• Current, active US Government Security Clearance of TS/SCI with Polygraph

Qualifications

• Basic Qualifications: 3+ years of programming in Python, Ruby, Go, Swift, Java, .Net, C++ or similar object oriented language experience; 3+ years of cloud architecture and solution implementation experience; Current, active US Government Security Clearance of TS/SCI with Polygraph
• Preferred Qualifications: Experience architecting and operating Kubernetes platforms on bare metal at scale, including lifecycle management, cluster provisioning, and day-2 operations (patching, upgrades, failure recovery) in air-gapped or high-security environments; Experience with SpectroCloud PaletteAI or similar bare-metal Kubernetes lifecycle management platforms; Hands-on experience with NVIDIA AI Enterprise, Run:AI, or equivalent GPU orchestration and scheduling platforms for large-scale AI/ML workloads, including multi-tenant resource management, gang scheduling for distributed training, and GPU fleet health monitoring; Demonstrated experience integrating multiple third-party software products into a unified platform stack, including managing cross-vendor compatibility, version dependencies, and developing cohesive operational procedures across components from OS through application layer; Experience developing and executing incremental deployment strategies for complex platforms, including automated provisioning, infrastructure-as-code, and validation testing at progressively larger scale in environments where no prior playbook exists