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AlluxioAlluxio

Technical Product Manager

Defines and executes AI systems strategy for Alluxio's data platform, focusing on inference, training, and agentic workloads. Collaborates with engineering and AI teams to optimize latency, throughput, and integrations with frameworks like PyTorch and Ray. Requires 5-9 years in AI infrastructure or ML platforms.

About the job

What You'll Own

  • AI Product Strategy — define the long-term vision and roadmap for Alluxio’s AI data platform, covering inference, training, and agentic workloads.
  • Systems Optimization for AI — collaborate with engineering to design features that deliver high-throughput, low-latency data access (e.g., GPU-aware caching, streaming reads, tiered prefetching).
  • AI Ecosystem Integration — ensure seamless integration with frameworks like PyTorch, TensorFlow, Ray, and Triton; evolve Alluxio’s APIs for AI-native workloads.
  • Customer & Partner Collaboration — engage directly with enterprise AI teams to understand workload patterns, validate impact, and prioritize roadmap direction.
  • Market Awareness & Foresight — stay ahead of trends in multi-model serving, retrieval-augmented generation (RAG), and agentic orchestration; translate them into actionable product plans.

What We’re Looking For

  • 5–9 years of experience in product management or technical leadership within AI infrastructure, ML platforms, or distributed systems.
  • Strong understanding of AI/ML workflows — from model training and deployment to inference and data-access pipelines.
  • Proven track record of delivering infrastructure features that improve latency, GPU utilization, or total cost of ownership.
  • Technical fluency with distributed systems, caching, and cloud orchestration (Kubernetes, AWS/GCP/Azure).
  • Familiarity with AI frameworks such as PyTorch, TensorFlow, Triton, Ray, or LangChain.
  • Exceptional communication and strategic thinking — ability to translate complex systems work into clear, prioritized roadmaps.

Skills

PyTorch, TensorFlow, Ray, Triton, Kubernetes, AWS, GCP, Azure, LangChain, Distributed Systems, Caching

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