●       Own the end-to-end lifecycle of our DevOps infrastructure, automating CI/CD pipelines to ensure repeatable, reliable, and secure system transitions.

●       Participate in the monitoring, debugging, and iterative improvement of production systems.

●       Partner closely with AI scientists to translate complex model requirements into robust, scalable engineering solutions.

●       Author and maintain clear, comprehensive documentation for system architectures, deployment workflows, and operational runbooks to ensure knowledge sharing and system maintainability.

Required Qualifications

●       Strong software engineering background with a focus on writing clean, maintainable, and well-tested code

●       Hands-on experience managing and scaling K8s in production (Pods, Networking, Services) and containerizing applications with Docker

●       Experience working with distributed systems or cloud-native architectures

●       Experience with cloud platforms (GCP or AWS)

●       Proficiency with Linux environments and command-line troubleshooting

●       Proficiency in Python for building backend systems and infrastructure tools

Preferred Qualifications

●       Experience supporting AI/ML training or inference workflows in production

●       Proficiency with CI/CD pipelines and Infrastructure as Code

●       Familiarity with monitoring, logging, and alerting systems

●       Experience in a startup or fast-growing environment