We are looking for a highly driven DevOps / Cloud Infrastructure Engineer to architect and scale mission-critical infrastructure supporting AI training, MLOps , Digital Twin environments, and large-scale monitoring systems.

🔹 About the Role

Training world-class Physical AI models requires a unique infrastructure ecosystem — massive GPU fleets, ultra-high-throughput storage, distributed computing, and secure sovereign deployments. You will play a foundational role in building and scaling this global infrastructure platform.

🔹 Key Responsibilities

• GPU Fleet Management: Architect and manage large-scale compute clusters, optimizing for performance, cost, and graceful failure handling.
• Distributed Computing: Deploy and scale frameworks for distributed training and inference across heterogeneous environments.
• Sovereign Infrastructure: Design "air-gapped" versions of our platform that can run entirely on-premises for privacy-conscious customers.
• Observability: Build comprehensive monitoring and alerting for complex ML workloads.

🔹 Key Qualifications

• Cloud & Orchestration: Expert-level knowledge of major cloud providers and container orchestration at scale.
• Distributed Systems: Proficiency in managing distributed training frameworks and high-throughput storage solutions.
• Automation: Mastery of Infrastructure-as-Code and modern CI/CD practices.
• Security: Deep understanding of network security and private/sovereign infrastructure design.
• 3–5 years of experience with Bachelor’s or Master’s Degree Computer Science Engineering / Artificial Intelligence / Data Engineering

🔹 Ideal Background

We are looking for engineers who have worked on:

• Large-scale AI/ML infrastructure platforms

• GPU-intensive compute environments

• Cloud-native distributed systems

• High-performance data infrastructure and MLOps ecosystems

• Secure enterprise or air-gapped deployments