Chief Ai Infrastructure Architect

1 week ago

Brisbane, Queensland, Australia Bebeemachinelearning Full time

Role Summary
A Technical Leader in AI Infrastructure Engineering
We are seeking a seasoned technical leader to head our AI infrastructure engineering efforts, focusing on building scalable and efficient AI platforms. This role requires expertise in designing and implementing foundational AI/ML infrastructure that supports multiple teams and use cases.
Architect and implement distributed data processing and training systems, optimizing for cost efficiency and developer experience.
Lead cross-functional initiatives to consolidate and improve dataset and training capabilities, working closely with stakeholders across engineering and research teams.
Build and maintain a robust and secure AI platform, making key technical decisions that impact model training and deployment.
Drive technical strategy discussions, weighing platform stability needs against product velocity requirements.
Mentor and grow other engineers, contributing to the growth of our AI platform engineering capabilities.
Solve complex technical challenges spanning multiple parts of the ML stack, staying up-to-date with industry trends and advancements.
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.
Proven track record of designing and implementing foundational AI/ML infrastructure that supports multiple teams and use cases.
Experience setting technical direction, making architectural decisions, and influencing engineering practices across organizations.
Strong understanding of distributed computing, container orchestration (Kubernetes), and cloud infrastructure (preferably AWS).
Fluent in Python with deep knowledge of ML frameworks (PyTorch, TensorFlow) and modern tools (W&B, Ray, Anyscale).
Strong understanding of generative AI systems, including LLMs, multimodal models, and foundation model fine-tuning.
Able to balance long-term platform investments with immediate business needs, making pragmatic technical decisions.
Experience working with product teams, research, and other engineering specialties to understand and address diverse technical requirements.
Track record of growing other engineers and contributing to technical culture and standards.
Experience with infrastructure-as-code and an understanding of performance optimization.
Consider GitOps principles for automation and deployment a plus.
A dynamic and collaborative work environment that fosters growth and innovation.
Ongoing opportunities for professional development and skill-building.
The chance to make meaningful contributions to cutting-edge AI projects and products.
Opportunities to collaborate with talented engineers and researchers from diverse backgrounds.
Flexible work arrangements that support work-life balance.
Competitive compensation and benefits package.
Recognition and rewards for outstanding performance and contributions.

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