Google Cloud Platform AI Platform Architect - Remote - CG

Role: Google Cloud Platform AI Platform Architect

Locations: 100% Remote

Duration: 12+ Months Contract

Job Description:

• We are building a new team of platform specialists tsupport and enhance high-performance AI services.
• These are highly technical, hands-on roles focused on customer, application, and platform support of AI-focused workloads.
• As an AI Platform Specialist, these roles will provide application and GPU support.
• The team will deliver Tier 1 and Tier 2 support tdevelopers and engineers while collaborating closely with Tier 3 and 4 platform teams and vendors for issue resolution.
• The roles require user knowledge of Kubernetes, virtualization, and cloud-native technologies as well as operator knowledge of GPUs and other AI supporting services.
• Each specialist should have a focus on customer service along with goals of reliability, scalability, and performance.

Key Responsibilities:

Platform Support & Incident Response:

• Provide Tier 1 & Tier 2 support for AI-driven applications and workloads.
• Troubleshoot and resolve issues related tKubernetes deployments, GPU utilization, and service performance.
• Collaborate with Tier 3+ teams, including Kubernetes engineers and external vendors, tescalate and resolve complex issues.

Kubernetes & Cloud-Native Operations:

• Full adoption, creation, and integrations intautomated services using Helm, Ansible, Terraform, etc.
• Deploy, manage, and support containerized AI workloads on Google Anthos-powered Kubernetes clusters.
• Ensure adherence tpod security policies, automated rollouts/rollbacks, and best practices for scalable and secure Kubernetes environments.

GPU Infrastructure & AI Services Management

• Optimize and support GPU-enabled workloads including CUDA and other AI acceleration frameworks.
• Assist in the installation, configuration, and support of AI coding assistants (e.g., Codeium).

Observability & Documentation:

• Maintain detailed operational documentation, runbooks, and troubleshooting guides.
• Utilize monitoring/logging tools like New Relic, Big Panda, Prometheus, Grafana, and other observability frameworks.

Process Improvement & Collaboration

• Work cross-functionally with developers, IT teams, and vendors tensure seamless deployment and support of AI services.
• Contribute tCI/CD pipelines, automation, service, and security best practices.
• Track and communicate work through task management platforms (ServiceNow and Jira).

Required Skills & Experience:

• Hybrid Cloud In-depth knowledge of private (on-premises) and public (Google Cloud Platform & AWS) cloud architectures and services.
• AI/ML Software Developer experience with DevOps practices (Git, Jenkins, etc.) as well as working with AI/ML engineers and data scientists.
• AI/ML Hardware Experience deploying, supporting, and optimizing on-premises and cloud GPUs (NVIDIA & AMD) enabled infrastructure (VMs & Containers).
• Kubernetes Expertise Hands-on experience with deploying and managing containerized workloads in Kubernetes.
• Technical Support & Troubleshooting Proven ability tdiagnose and resolve customer and platform issues in production environments.
• Strong Communication & Documentation Ability tclearly document procedures, write knowledge base articles, and collaborate with customers and teams.
• Time Management & Accountability Ability twork independently, prioritize tasks, and manage workload effectively.

Preferred Qualifications:

• Experience with GPU orchestration tools like Run:AI, NVIDIA AI Enterprise, VMWare Private AI Foundation, etc.
• Exposure tAI coding assistants like Codeium, Copilot, or Tabnine.
• Proficient in development tools like Python, PyTorch, TensorFlow, Jupyter Notebooks, etc.

About the Team & Reporting Structure:

• These positions will report tthe Senior AI Architect and work as peers within a specialized AI support team.
• Collaboration with internal VM and container support teams as well as NVIDIA, Codeium, and other vendor specialists will be essential for supporting customers, troubleshooting, and optimizing AI workloads.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Role: Google Cloud Platform AI Platform Architect

Locations: 100% Remote

Duration: 12+ Months Contract

Job Description:

• We are building a new team of platform specialists tsupport and enhance high-performance AI services.
• These are highly technical, hands-on roles focused on customer, application, and platform support of AI-focused workloads.
• As an AI Platform Specialist, these roles will provide application and GPU support.
• The team will deliver Tier 1 and Tier 2 support tdevelopers and engineers while collaborating closely with Tier 3 and 4 platform teams and vendors for issue resolution.
• The roles require user knowledge of Kubernetes, virtualization, and cloud-native technologies as well as operator knowledge of GPUs and other AI supporting services.
• Each specialist should have a focus on customer service along with goals of reliability, scalability, and performance.

Key Responsibilities:

Platform Support & Incident Response:

• Provide Tier 1 & Tier 2 support for AI-driven applications and workloads.
• Troubleshoot and resolve issues related tKubernetes deployments, GPU utilization, and service performance.
• Collaborate with Tier 3+ teams, including Kubernetes engineers and external vendors, tescalate and resolve complex issues.

Kubernetes & Cloud-Native Operations:

• Full adoption, creation, and integrations intautomated services using Helm, Ansible, Terraform, etc.
• Deploy, manage, and support containerized AI workloads on Google Anthos-powered Kubernetes clusters.
• Ensure adherence tpod security policies, automated rollouts/rollbacks, and best practices for scalable and secure Kubernetes environments.

GPU Infrastructure & AI Services Management

• Optimize and support GPU-enabled workloads including CUDA and other AI acceleration frameworks.
• Assist in the installation, configuration, and support of AI coding assistants (e.g., Codeium).

Observability & Documentation:

• Maintain detailed operational documentation, runbooks, and troubleshooting guides.
• Utilize monitoring/logging tools like New Relic, Big Panda, Prometheus, Grafana, and other observability frameworks.

Process Improvement & Collaboration

• Work cross-functionally with developers, IT teams, and vendors tensure seamless deployment and support of AI services.
• Contribute tCI/CD pipelines, automation, service, and security best practices.
• Track and communicate work through task management platforms (ServiceNow and Jira).

Required Skills & Experience:

• Hybrid Cloud In-depth knowledge of private (on-premises) and public (Google Cloud Platform & AWS) cloud architectures and services.
• AI/ML Software Developer experience with DevOps practices (Git, Jenkins, etc.) as well as working with AI/ML engineers and data scientists.
• AI/ML Hardware Experience deploying, supporting, and optimizing on-premises and cloud GPUs (NVIDIA & AMD) enabled infrastructure (VMs & Containers).
• Kubernetes Expertise Hands-on experience with deploying and managing containerized workloads in Kubernetes.
• Technical Support & Troubleshooting Proven ability tdiagnose and resolve customer and platform issues in production environments.
• Strong Communication & Documentation Ability tclearly document procedures, write knowledge base articles, and collaborate with customers and teams.
• Time Management & Accountability Ability twork independently, prioritize tasks, and manage workload effectively.

Preferred Qualifications:

• Experience with GPU orchestration tools like Run:AI, NVIDIA AI Enterprise, VMWare Private AI Foundation, etc.
• Exposure tAI coding assistants like Codeium, Copilot, or Tabnine.
• Proficient in development tools like Python, PyTorch, TensorFlow, Jupyter Notebooks, etc.

About the Team & Reporting Structure:

• These positions will report tthe Senior AI Architect and work as peers within a specialized AI support team.
• Collaboration with internal VM and container support teams as well as NVIDIA, Codeium, and other vendor specialists will be essential for supporting customers, troubleshooting, and optimizing AI workloads.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.