AI Platform Specialist
AI Platform Specialists – Remote with EST Time Working Hrs.
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 issue 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 and NVIDIA, Codeium, and other vendor specialists will be essential for supporting customers, troubleshooting, and optimizing AI workloads.
Reach out by Tuesday 11 Feb 2025 to hr@headstartsolutions.org
