We're building the infrastructure layer for agentic web interaction at scale. Our API is designed from the ground up to power Retrieval-Augmented Generation (RAG) and real-time reasoning in AI systems. By connecting LLMs to high-quality, trustworthy web content, we help developers build agents that are not only intelligent - but also informed.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 EngineerManaging Kubernetes clusters across multiple environments and regionsOwning infrastructure as code for all resourcesMaintaining and improving CI/CD pipelines and GitOps-based deploymentsMaintaining and optimize real-time data pipelines that process billions of events per day across distributed queues and stream processorsBuilding out monitoring, alerting, and observabilityDebugging production issues across servicesManaging cloud costs and capacity planningWorking closely with a small engineering team - you'd own infra, not a slice of itRequirements: 3+ years in a DevOps or platform engineering role, working in production environmentsProven experience designing and operating large-scale, distributed systems, with a solid understanding of API design, reliability, and performance at scaleStrong Kubernetes experience in a managed cloud environmentProficiency with infrastructure as code (Terraform or similar)Experience with GitOps-based deployment workflowsBuilt or maintained observability stacks (logging, metrics, alerting)Experience handling production incidents calmly and methodicallyThis position is open to all candidates.
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