AI Infrastructure Architecture Manager

About the role

As a Lead and Principal Infrastructure Architect you will own the end‑to‑end design of compute infrastructure for large‑scale AI and machine‑learning systems, including distributed training environments. You will translate business goals, SLAs and client standards into cost‑efficient, high‑performance architectures.

Key responsibilities

• Design and optimise the full computational stack for performance, power, cost and scalability.
• Architect large‑scale GPU clusters and distributed training systems, selecting accelerators, networking and storage.
• Lead architecture assessments, identify gaps and recommend remediation.
• Define and maintain the AI infrastructure roadmap, capacity planning and technology evolution.
• Establish deployment, automation and CI/CD strategies for AI systems and data pipelines.
• Set monitoring, observability and cost‑tracking practices across InfraOps and MLOps.

Required profile

• Bachelor’s degree in Computer Science, Computer Engineering or a related engineering field.
• Significant experience building, monitoring and troubleshooting AI/ML model deployments on‑premise or public cloud.
• Proven experience leading AI/ML infrastructure projects on at least one hyperscaler (AWS, Azure or GCP).
• Strong problem‑solving, communication and project‑management skills.

Required skills

• Programming: Python, Java, C++
• Cloud platforms: AWS, Microsoft Azure, Google Cloud Platform
• Data pipeline and workflow tools: Apache Airflow, Kubeflow
• GPU cluster design and distributed training
• CI/CD and automation frameworks