Responsibilities

• Design, deploy, and maintain GPU/TPU clusters, high-performance computing systems, and cloud infrastructure for AI workloads.
• Build and optimise data pipelines to support training and inference for large AI models.
• Collaborate with ML engineers to deploy models efficiently at scale.
• Monitor and troubleshoot infrastructure performance, availability, and security.
• Automate workflows and infrastructure using CI/CD and Infrastructure-as-Code tools.
• Evaluate and integrate emerging AI infrastructure technologies and frameworks.
• Ensure cost-efficient, reliable, and scalable AI operations across production and research environments.
• Maintain documentation for systems, workflows, and best practices.

Requirements

• Strong experience with cloud platforms (AWS, GCP, Azure) and GPU/TPU-based computing.
• Proficiency in scripting languages (Python, Bash, etc.) for automation.
• Experience with containerisation and orchestration (Docker, Kubernetes).
• Familiarity with MLOps and AI deployment pipelines.
• Understanding of distributed training and parallel computing.
• Knowledge of storage solutions and high-performance networking for AI workloads.
• Problem-solving mindset and ability to work in a fast-paced, collaborative environmen

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