Site Reliability Engineer

• Reliability & Availability: Ensure uptime, resiliency, and fault tolerance of AI model training and inference systems
• Observability: Design and maintain monitoring, alerting, and logging systems to provide real-time visibility into model serving pipelines and infra
• Performance Optimization: Analyze system performance and scalability, optimize resource utilization (compute, GPU clusters, storage, networking)
• Automation & Tooling: Build automation for deployments, incident response, scaling, and failover in hybrid cloud/on-prem CPU+GPU environments
• Incident Management: Lead on-call rotations, troubleshoot production issues, conduct blameless postmortems, and drive continuous improvements
• Security & Compliance: Ensure data privacy, compliance, and secure operations across model training and serving environments
• Collaboration: Partner with ML engineers and platform teams to improve developer experience and accelerate research-to-production workflows

• 4+ years of experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering roles
• Strong proficiency in Kubernetes, Docker, and container orchestration
• Knowledge of CI/CD pipelines for Inference and ML model deployment
• Hands-on experience with public cloud platforms like Azure/AWS/GCP and infrastructure-as-code
• Expertise in monitoring & observability tools (Grafana, Datadog, OpenTelemetry, etc.)
• Strong programming/scripting skills in Python, Go, or Bash
• Solid knowledge of distributed systems, networking, and storage

• Experience running large-scale GPU clusters for ML/AI workloads
• Familiarity with ML training/inference pipelines
• Experience with high-performance computing (HPC) and workload schedulers (Kubernetes operators)
• Background in capacity planning & cost optimization for GPU-heavy environments

Competitive compensation, equity options, and comprehensive benefits