About the Role
Are you passionate about building the systems that power modern cloud applications and high-throughput data platforms?

We are looking for an experienced Cloud Infrastructure Engineer to help build, scale, and maintain our enterprise computing platform. In this role, you will be the core engineer behind the scenes—ensuring our systems have the processing power, storage speed, and automated scaling required to run complex workloads smoothly and efficiently.

What You Will Do
Kubernetes & Cluster Management: Manage, scale, and optimize our production Kubernetes clusters (node pool strategies, HPA/VPA autoscaling, ingress/load balancing, and security baselines).

Resource & Workload Scheduling: Implement automated queuing, priority scheduling, and resource allocation (GPUs/CPUs) for parallel computing frameworks (e.g., Ray).

High-Performance Storage: Optimize multi-tier storage setups (Object Storage, high-speed file systems, and local caching) so large datasets (e.g., LanceDB) load in seconds.

CI/CD & Automation: Design and maintain automated build and deployment pipelines supporting hybrid hardware environments (Arm / x86 architecture compatibility).

Observability & Reliability: Monitor platform health using modern metrics and logging tools, set up alerts, and build backup systems to ensure high availability and fast incident response.

Requirements
Education: Bachelor’s Degree in Computer Science, Software Engineering, Information Technology, or a related technical field.

Professional Experience: Proven hands-on experience in cloud infrastructure, DevOps, or system engineering roles.

Cloud & Containers: Strong expertise with Docker, Kubernetes cluster operation/maintenance, and core cloud networking.

Linux & Scripting: Advanced proficiency in Linux administration with solid scripting skills in Python, Shell, or Go.

Storage Concepts: Practical working knowledge of Object Storage (S3-compatible), block storage, and high-performance file systems.

Problem Solver: Strong background in troubleshooting performance bottlenecks and optimizing system efficiency in production environments.