We work with some of the most innovative teams in AI – from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it's powering sales assistants, research copilots, or internal knowledge tools, we're the missing link between LLMs and the real world.
The Role: DevOps Engineer
Managing Kubernetes clusters across multiple environments and regions
Owning infrastructure as code for all resources
Maintaining and improving CI/CD pipelines and GitOps-based deployments
Maintaining and optimize real-time data pipelines that process billions of events per day across distributed queues and stream processors
Building out monitoring, alerting, and observability
Debugging production issues across services
Managing cloud costs and capacity planning
Working closely with a small engineering team – you'd own infra, not a slice of it
3+ years in a DevOps or platform engineering role, working in production environments
Proven experience designing and operating large-scale, distributed systems, with a solid understanding of API design, reliability, and performance at scale
Strong Kubernetes experience in a managed cloud environment
Proficiency with infrastructure as code (Terraform or similar)
Experience with GitOps-based deployment workflows
Built or maintained observability stacks (logging, metrics, alerting)
Experience handling production incidents calmly and methodically
