Site Reliability Engineer

Who you are

• Solid hands-on Kubernetes knowledge, including workloads, resource management, and production operations
• Strong experience with Infrastructure as Code, and the lifecycle of cloud infrastructure
• Solid experience building and operating CI/CD pipelines and automated deployment workflows
• Hands-on experience with at least one major cloud provider: GCP, AWS or Azure
• Good understanding of networking, distributed systems and reliability engineering
• Experience with monitoring, observability and troubleshooting production systems
• Strong automation mindset and the ability to take ownership of well-defined production systems and progressively tackle more complex problems
• Excellent written and spoken English
• Go and/or Python engineering experience
• Exposure to AI/ML infrastructure and inferences
• Comfortable working AI-first, using coding agents, agentic workflows and AI-assisted debugging to accelerate engineering and operations

What the job involves

• AI Platform builds and operates the shared production foundations supporting Algolia's evolving AI ecosystem
• The team works at the intersection of Site Reliability Engineering, cloud infrastructure, software engineering and AI, helping engineering teams bring AI-powered capabilities to production reliably, securely and efficiently
• Our scope includes Kubernetes, cloud infrastructure, CI/CD, networking, databases, observability, reliability, FinOps and production operations
• We are looking for a Site Reliability Engineer with strong production fundamentals who enjoys solving operational problems, automating repetitive work and progressively taking ownership of complex systems at scale
• Build and operate production infrastructure supporting AI-related workloads and services
• Operate and improve highly available Kubernetes-based platforms
• Improve reliability through SLOs, observability, alerting and capacity management
• Investigate production issues and turn findings into durable fixes and improvements
• Work across networking, databases, compute and service infrastructure
• Improve CI/CD pipelines, deployment automation and developer experience
• Build and maintain infrastructure using Infrastructure as Code
• Participate in on-call, incident response and operational improvements
• Collaborate with experienced engineers across AI Platform and progressively take ownership of broader production areas