Key Responsibilities

• Take ownership of the stability and completion rate of large-scale AI computing workloads, establishing clear performance targets and continuously improving operational outcomes.
• Design and maintain resource allocation mechanisms for accelerated computing environments, including workload prioritization, capacity balancing, congestion control, and automated recovery.
• Optimize the use of high-performance computing resources through isolation and resource-sharing strategies while maintaining workload stability.
• Analyze model execution patterns, resource consumption, processing time, and failure scenarios to improve workload sizing and minimize memory-related interruptions.
• Establish standardized delivery and runtime frameworks that enable new AI models to be integrated efficiently and consistently.
• Develop end-to-end operational visibility through monitoring, centralized logging, distributed tracing, alerting, and diagnostic capabilities.
• Improve the resilience of distributed execution platforms by strengthening workflow management, fault tolerance, retry mechanisms, and service recovery.
• Collaborate with infrastructure and AI development teams to ensure computing environments and machine-learning solutions are scalable and production-ready.

Qualifications

Essential Requirements

• 5+ years of experience in AI infrastructure, machine-learning platforms, distributed computing, or production engineering, with responsibility for system availability and operational performance.
• Strong hands-on experience managing large-scale accelerated computing environments and related hardware/software ecosystems.
• Advanced knowledge of distributed workload management, resource scheduling, and containerized execution platforms.
• Practical experience with enterprise cloud-based machine-learning services and familiarity with multi-cloud environments.

• Hands-on experience with managed ML platforms on at least one major cloud provider, along with practical familiarity with the other:

  • GCP: Cloud Run, Vertex AI
  • AWS: SageMaker
• Demonstrated experience improving the efficiency, scalability, and reliability of diverse high-performance computing environments.
• Strong programming capability in Python, together with experience in at least one lower-level or backend-oriented language; able to develop internal platforms and automation tools rather than relying solely on configuration.
• Good understanding of production reliability practices, including service-level objectives, system monitoring, distributed tracing, operational troubleshooting, and incident management.