AI Infrastructure Engineer

You will work closely with ML engineers, data scientists, and DevOps teams to support model training, deployment, and high-performance AI operations.

Responsibilities Design, implement, and manage cloud and on-prem infrastructure for AI/ML training and inference. Build and maintain GPU/TPU clusters, high-performance computing environments, and distributed systems. Develop and optimise data pipelines to support large-scale model training. Work closely with ML engineers to deploy and operationalise AI models in production. Improve system performance, reliability, and efficiency for AI workloads. Automate infrastructure using IaC and CI/CD tools.

Requirements Strong experience with cloud platforms (AWS, GCP, Azure) and GPU-based compute. Proficiency with Linux, scripting (Python/Bash), and infrastructure automation. Hands-on experience with Kubernetes, Docker, and container orchestration. Solid understanding of distributed systems, networking, and storage for AI workloads. Experience building or supporting ML training/inference pipelines. Familiarity with MLOps tools, experiment tracking, and monitoring frameworks. Ability to work cross-functionally in fast-paced engineering environments.