About the Role
We are seeking a Software Engineer to own the lifecycle of the production-grade software systems that support AI workflows. This role will drive the evolution of our microservices architecture, leveraging containerization and Kubernetes to build stable, scalable distributed systems across our Linux-based environments.
This is a hands-on role for engineers who enjoy working close to production systems and want to grow their expertise in cloud-native and AI-driven platforms.
Key Responsibilities
● Design, build, and maintain standalone, production-grade software systems that serve as the foundation for AI workflows.
● Lead the deployment and management of services using Kubernetes, ensuring high availability and seamless scaling of containerized workloads.
● 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
