Site Reliability Engineer, Kraken

• 5+ years of experience as a Site Reliability Engineer, Infrastructure Engineer, Platform Engineer, or similar role in a production environment
• Hands-on experience supporting ML infrastructure, model serving, or MLOps workflows in production
• Experience building developer platforms, internal tooling, APIs, or SDKs consumed by engineering teams at scale
• Strong understanding of platform engineering principles, including developer experience, self-service infrastructure, and API-driven platform design
• Proficiency with Infrastructure as Code tools, particularly Terraform
• Experience with containerization and orchestration, particularly Kubernetes and Docker
• Solid understanding of cloud infrastructure, preferably AWS
• Strong scripting skills (bash/shell) and proficiency in at least one programming language (Python preferred)
• Experience designing and operating observability, monitoring, and alerting systems
• Experience implementing incident response procedures and participating in on-call rotations
• Strong collaboration skills working across data, AI, and engineering teams
• High ownership mindset in a fast-moving, high-stakes production environment
• We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto

Desirable

• Design, build, and operate the infrastructure layer supporting AI agent workflows in production
• Ensure reliability, scalability, and observability of agentic systems across internal and external products
• Design and develop platform services, APIs, SDKs, and self-service capabilities that allow engineering teams to easily consume AI infrastructure and agent platform services
• Manage and maintain the compute, orchestration, and serving infrastructure powering model inference and agent execution
• Implement robust monitoring, alerting, and incident response procedures tailored to AI/ML workloads
• Utilize Infrastructure as Code (IaC) tools such as Terraform to provision and manage cloud (AWS) infrastructure components
• Build and maintain CI/CD pipelines that support rapid, reliable deployment of AI services and agent workflows
• Define and implement guardrails, failure handling, and recovery patterns specific to agentic and LLM-powered systems
• Collaborate with AI and Data Engineering teams to translate experimental agent prototypes into hardened production systems
• Manage containerized workloads using Kubernetes, ensuring efficient deployment, scaling, and orchestration of AI services
• Implement access controls and security best practices across AI infrastructure environments
• Document architecture, runbooks, and best practices to support knowledge sharing across the team