Expert Cloud Architect Specialist

5 days ago

Lucknow, Uttar Pradesh, India beBeeCloud Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
Job Title: Cloud Infrastructure Engineer

Role: Design, implement and optimize cutting-edge infrastructure for machine learning workloads.

Experience: 6-8 years of experience in developing ML infrastructure.

As a Platform Architect, you will play a pivotal role in designing and implementing scalable automation infrastructure to support growing platform capabilities with consistent test environments.

  • Design state-of-the-art GPU compute clusters to support critical workloads.
  • Develop robust performance testing frameworks to validate platform scalability, resilience, and identify optimization opportunities.
  • Plan comprehensive monitoring solutions with alerting systems to track platform health and ensure SLA compliance.
  • Design specialized test frameworks for security controls and ensure compliance validation across payment and personal data.

Key Responsibilities:

  • Design and implement scalable automation infrastructure that supports growing platform capabilities with consistent test environments.
  • Troubleshoot, diagnose, and perform root cause analysis of system failures, isolating components and failure scenarios in collaboration with internal and external partners.
  • Optimize cluster operations for maximum reliability, efficiency, and performance.

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 across various mediums.

Desirable Skills:

  • Significant experience with sophisticated infrastructure tooling, including Kubernetes Cluster API, Terraform, Helm, and Operator Framework.
  • Practical, production-level 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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