Highly Skilled Infrastructure Architect

Job Summary

We are seeking a highly skilled and passionate Platform Engineer to join our dynamic team as an Infrastructure Architect. As a key member of our team, you will be instrumental in designing, developing, and maintaining cutting-edge build and test environments for critical GenAI workloads running on foundational cloud infrastructure.

You will partner with architects to design and implement highly robust and scalable systems, while also providing crucial development support to SRE/Operations teams as they tackle complex distributed systems challenges at scale. We're seeking an engineer who champions Quantiphi's dedication to Cloud-Native development, with a particular emphasis on Kubernetes.

Key Responsibilities:

• Designing and Implementing State-of-the-Art GPU Compute Clusters: You will design and implement state-of-the-art GPU compute clusters to support critical workloads.
• Automated Testing Strategies and Frameworks: You will design comprehensive automated testing strategies and frameworks across unit, integration, API, and end-to-end levels for critical commerce flows.
• Performance Testing Frameworks: You will develop robust performance testing frameworks to validate platform scalability, resilience, and identify optimization opportunities.
• Comprehensive Monitoring Solutions: You will plan comprehensive monitoring solutions with alerting systems to track platform health and ensure SLA compliance.
• Specialized Test Frameworks: You will design specialized test frameworks for security controls and ensure compliance validation across payment and personal data.
• Scalable Automation Infrastructure: You will architect a scalable automation infrastructure that supports growing platform capabilities with consistent test environments.
• Troubleshooting and Root Cause Analysis: You will troubleshoot, diagnose, and perform root cause analysis of system failures, isolating components and failure scenarios in collaboration with internal and external partners.
• Cluster Operations Optimization: You will optimize cluster operations for maximum reliability, efficiency, and performance.

Required Skills and Qualifications

• 6-8 Years of Experience in ML Infrastructure: Over 6-8 years of experience working with developing ML Infrastructure.
• Large-Scale Experience with Kubernetes: Over 3 years of hands-on experience in large-scale direct experience building and deploying production-ready services on Kubernetes.
• Open-Source Contributions: A proven history of engaging with and contributing to open-source projects.
• Collaborative Spirit: A collaborative spirit, demonstrated by prior work developing scalable software solutions for cloud services.
• Effective Communication: The ability to effectively communicate complex technical designs and quality approaches across various mediums.
• GPU Computing and AI Infrastructure: A deep understanding of GPU computing and AI infrastructure.
• Technical Challenges and System Performance: A strong passion for solving complex technical challenges and optimizing system performance.
• Container Technologies and Programming Languages: Working knowledge of cluster configuration management tools such as BCM or Ansible, and infrastructure-level applications including Kubernetes, Terraform, and MySQL. Proficiency in programming with Python and Bash scripting.

Benefits and Advantages

• Sophisticated Infrastructure Tooling: Significant experience with sophisticated infrastructure tooling, including Kubernetes Cluster API, Terraform, Helm, and Operator Framework.
• Major Cloud Platforms: Practical, production-level experience across major cloud platforms: Azure, Google Cloud Platform (GCP), or Amazon Web Services (AWS).
• Adaptability to New Technologies: Ability to adapt to new technologies and Frameworks in ML/GenAI landscape.
• Software Refactoring and Optimization: A strong track record of successfully refactoring and optimising software for deployment within Kubernetes environments.
• Kubernetes Concepts: Comfort discussing and working with core Kubernetes concepts like CSI, CNI, and CRI.
• CNCF Landscape and Associated Tooling: Comprehensive understanding of the CNCF landscape and its associated tooling.
• Complex Problem Decomposition: The ability to decompose complex problems into simpler sub-problems and leverage existing solutions for efficient implementation, along with designing simple, self-sustaining systems.
• AI/ML Incident Detection and Resolution: Experience leveraging AI/ML to proactively detect and resolve incidents, automate alert triaging, perform log analysis, and streamline repetitive workflows.