You will work closely with ML engineers, data scientists, and Dev Ops teams to ensure scalable, high-performance, and reliable AI pipelines.
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 environment.