Urgent Need AI Platform Specialists at Atlanta, GA (Hybrid Opportunity)

Hi,

Pleasure mailing you. Please go through the below requirement and let me know if you are comfortable with the position.

Please send me your updated resume along with the best hourly rate, work authorization status and availability.

An early response is really appreciated.

Job Title : AI Platform Specialists

Location : Atlanta, GA (Hybrid Opportunity)

Duration : 12 Months

AI Platform Specialists

We are building a new team of platform specialists to support 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 to developers 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 to Kubernetes deployments, GPU utilization, and service performance.
Collaborate with Tier 3+ teams, including Kubernetes engineers and external vendors, to escalate and resolve complex issues.

Kubernetes & Cloud-Native Operations

Full adoption, creation, and integrations into automated services using Helm, Ansible, Terraform, etc.
Deploy, manage, and support containerized AI workloads on Google Anthos-powered Kubernetes clusters.
Ensure adherence to pod 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 to ensure seamless deployment and support of AI services.
Contribute to CI/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 (GCP & 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 to diagnose and resolve customer and platform issues in production environments.
Strong Communication & Documentation – Ability to clearly document procedures, write knowledge base articles, and collaborate with customers and teams.
Time Management & Accountability – Ability to work 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 to AI 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 to the 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.

Thanks & Regards,

Suresh Kumar Reddy Kandula

Lead - US IT

E: Sureshr@tekskillsinc.com