Senior Site Reliability Engineer, Corelight

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field, or equivalent experience
• 6+ years in SRE, DevOps, Platform Engineering, MLOps, or Cloud Infrastructure roles
• 3+ years building software infrastructure in a disributed systems architecture environment
• 3+ years of production experience with Kubernetes (EKS, GKE, AKS) and containerization tools like Docker
• Strong programming skills in Python and proficiency in Bash, Go, or PowerShell
• Proficiency with Infrastructure-as-Code tools (Terraform, CloudFormation)
• Experience with Kubernetes Operators, Helm, GitOps (ArgoCD, Flux), or Service Mesh (Istio, Linkerd)
• Exposure to serverless compute (AWS Lambda, Azure Functions)
• Experience building or automating data and model pipelines for AI/ML/LLM workloads (e.g., RAG, fine-tuning, inference)
• Strong understanding of observability and monitoring using Prometheus, Grafana, ELK/EFK, Langfuse, or similar platforms
• Familiarity with SLI/SLO/SLA practices, incident response, and reliability engineering in production environments

Desirable

• Towards the end of our interview process is an in-person interview
• As a Senior Site Reliability Engineer, you will design, automate, and scale cloud and hybrid platforms that power AI/ML workloads and SaaS services
• You’ll collaborate with engineering teams to build reliable, secure, and observable infrastructure, manage Kubernetes environments, and enable CI/CD pipelines for continuous delivery of AI models and applications at scale
• Your expertise in cloud, DevOps, and MLOps will drive performance, uptime, and innovation across production systems
• Design, deploy, and scale AI/ML/LLM infrastructure across cloud platforms (AWS, Azure, or GCP) ensuring high reliability and performance
• Manage and optimize Kubernetes environments (EKS, AKS, GKE) for AI services, data pipelines, and model operations
• Build and automate end-to-end data and model pipelines for fine-tuning, inference, and RAG workloads using Terraform, Python, and CI/CD tooling
• Utilize automation tools such as GitOps, CI/CD pipelines, and containerization technologies (Docker, Kubernetes) to streamline ML/LLM tasks across the Large Language Model lifecycle
• Implement monitoring, observability, and reliability best practices using Prometheus, Grafana, ELK/EFK, Langfuse, and SLI/SLO/SLA frameworks
• Lead incident response, performance tuning, and cost optimization across AI infrastructure and production workloads