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Infrastructure Specialist

2 weeks ago

Agra, Uttar Pradesh, India beBeeMLOps Full time ₹ 1,50,00,000 - ₹ 2,50,00,000
Job Title:

MLOps Architect


Description:

We are seeking a seasoned Platform Architect to spearhead the design and implementation of cutting-edge build and test environments for critical GenAI workloads.

In this role, you will be instrumental in designing, developing, and maintaining production-scale platforms within the GenAI or ML domain. You will partner with architects to design and implement highly robust and scalable systems while providing crucial development support to SRE/Operations teams as they tackle complex distributed systems challenges at scale.

Our ideal candidate has over 6-8 years of experience working with developing ML Infrastructure, hands-on experience building and deploying production-ready services on Kubernetes, and a proven history of engaging with and contributing to open-source projects.

A collaborative spirit, demonstrated by prior work developing scalable software solutions for cloud services, is essential. The ability to effectively communicate complex technical designs and quality approaches across various mediums is also required.

Key Responsibilities:

  • Designing and implementing state-of-the-art GPU compute clusters to support critical workloads.
  • Designing comprehensive automated testing strategies and frameworks across unit, integration, API, and end-to-end levels for critical commerce flows.
  • Developing robust performance testing frameworks to validate platform scalability, resilience, and identify optimization opportunities.
  • 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 in collaboration with internal and external partners.
  • Optimizing cluster operations for maximum reliability, efficiency, and performance.

Required Skills:

  • Over 6-8 years of experience working with developing ML Infrastructure.
  • Over 3 years of 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.
  • A collaborative spirit, demonstrated by prior work developing scalable software solutions for cloud services.
  • The ability to effectively communicate complex technical designs and quality approaches across various mediums.
  • A deep understanding of GPU computing and AI infrastructure.
  • A strong passion for solving complex technical challenges and optimizing system performance.
  • Working knowledge of cluster configuration management tools such as BCM or Ansible, and infrastructure-level applications including Kubernetes, Terraform, and MySQL.
  • In-depth understanding of container technologies like Docker and Containers.
  • Proficiency in programming with Python and Bash scripting.

Preferred Qualifications:

  • 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.
  • A strong track record of successfully refactoring and optimising software for deployment within Kubernetes environments.
  • Comfort discussing and working with core Kubernetes concepts like CSI, CNI, and CRI.
  • Comprehensive understanding of the CNCF landscape and its associated tooling.
  • The ability to decompose complex problems into simpler sub-problems and leverage existing solutions for efficient implementation, along with designing simple, self-sustaining systems.
  • Experience leveraging AI/ML to proactively detect and resolve incidents, automate alert triaging, perform log analysis, and streamline repetitive workflows.