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AI Infrastructure Architect

3 weeks ago

Brisbane, Queensland, Australia beBeeInfrastructure Full time $200,000 - $260,000
Job Title: AI Infrastructure Architect

An AI Infrastructure Architect is required to design and implement foundational AI/ML infrastructure, focusing on the dataset & training platform team. The role involves architecting capabilities, making key technical decisions, and leading cross-team initiatives to improve dataset and training capabilities.

The ideal candidate will have over 5 years of experience building and scaling ML training systems, with hands-on experience in distributed training, model lifecycle management, and large-scale data processing. They will also possess a strong understanding of distributed computing, container orchestration, and cloud infrastructure, as well as fluency in Python and knowledge of ML frameworks such as PyTorch and TensorFlow.

Key Responsibilities:
  • Architecting AI Platform Capabilities: Design and implement foundational AI/ML infrastructure that supports multiple teams and use cases.
  • Leading Cross-Team Initiatives: Lead cross-team initiatives to consolidate and improve dataset and training capabilities, working with stakeholders across multiple engineering and research teams.
  • Paved Roads for Distributed Data Processing: Build and maintain paved roads for high-performance distributed data processing and training systems, optimising for cost efficiency and developer experience.
  • Technical Strategy Discussions: Drive technical strategy discussions, weighing platform stability needs against product velocity requirements.
  • Mentoring Engineers: Mentor other engineers and contribute to growing the AI platform engineering capabilities.
You're Probably a Match If:
  • You have a proven track record of designing and implementing foundational AI/ML infrastructure.
  • You have experience setting technical direction, making architectural decisions, and influencing engineering practices across organisations.
  • You possess a strong understanding of generative AI systems, including LLMs, multimodal models, and foundation model fine-tuning.
  • You have the ability to balance long-term platform investments with immediate business needs, making pragmatic technical decisions.