Platform Architect for GenAI Workloads

4 days ago

Gandhinagar, Gujarat, India beBeeInfrastructure Full time US$ 15,00,000 - US$ 20,00,000

As a seasoned platform architect, you'll be instrumental in designing and developing cutting-edge build and test environments for critical GenAI workloads. In this role, you'll partner with architects to design and implement highly robust and scalable systems.

Key Responsibilities:
  • Designing state-of-the-art GPU compute clusters to support critical workloads.
  • Developing comprehensive automated testing strategies and frameworks across unit, integration, API, and end-to-end levels.
  • Planning of comprehensive monitoring solutions with alerting systems to track platform health and ensure SLA compliance.
  • Designing specialized test frameworks for security controls and ensuring compliance validation across payment and personal data.
  • Architecting a scalable automation infrastructure that supports growing platform capabilities with consistent test environments.
  • Troubleshooting, diagnosing, and performing root cause analysis of system failures, isolating components and failure scenarios.

Requirements:

  • Over 6-8 years of experience working with developing ML Infrastructure.
  • Hands-on experience in large-scale direct experience building and deploying production-ready services on Kubernetes.
  • A proven history of engaging with and contributing to open-source projects.
  • Collaborative spirit, demonstrated by prior work developing scalable software solutions for cloud services.
  • Effective communication of complex technical designs and quality approaches.
  • Deep understanding of GPU computing and AI infrastructure.
  • Strong passion for solving complex technical challenges and optimizing system performance.
  • Working knowledge of cluster configuration management tools like BCM or Ansible.

Ways To Stand Out From The Crowd:

  • Significant experience with sophisticated infrastructure tooling, including Kubernetes Cluster API, Helm, and Operator Framework.
  • Practical experience across major cloud platforms: Azure, Google Cloud Platform (GCP), or Amazon Web Services (AWS).
  • Ability to adapt to new technologies and Frameworks in ML/GenAI landscape.

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