AI Site Reliability Engineer

What you'll do

• Design and operate highly available infrastructure for AI training, inference, and data-processing workloads.
• Define and manage service-level objectives, reliability metrics, error budgets, and capacity plans.
• Build monitoring, logging, tracing, and AI-specific observability for model-serving platforms.
• Lead incident response, root-cause analysis, and post-incident reliability improvements.
• Automate deployment, scaling, disaster recovery, and routine operational tasks.
• Operate Kubernetes-based services, cloud platforms, GPU clusters, networking, and storage.
• Improve model-serving performance regarding latency, throughput, GPU utilization, and cost efficiency.
• Establish operational standards for model versioning, canary releases, and rollback procedures.

What we're looking for

• 4+ years of experience in SRE, infrastructure engineering, DevOps, platform engineering, or production engineering.
• Strong experience in Linux, networking, cloud, and distributed systems.
• Hands-on experience with Kubernetes and infrastructure-as-code tools such as Terraform.
• Experience with at least one major cloud platform: AWS, Azure, or Google Cloud.
• Proficiency in Python, Go, or another programming language used for automation and production systems.
• Experience with observability platforms, metrics, logs, traces, and incident-management practices.
• Experience operating production services with defined availability and performance targets.
• Experience operating machine-learning or generative-AI workloads in production (preferred).