Site Reliability Engineer - AI Agents

Kraken

3d • $96,000 - $192,000

About The Position

The AI Infrastructure team sits within the Data organization and is responsible for building, operating, and scaling the systems that power AI agents in production — both internal tools and external-facing products. Working closely with the AI and Agent Systems teams, this group ensures that the orchestration, execution, and model-serving layers underpinning agentic workflows are reliable, observable, and built to scale. This team operates at the intersection of data infrastructure and applied AI — a space that moves fast and demands engineers who can bring production discipline to emerging technology. You'll partner across Data Engineering, ML, and product-facing teams to harden agent infrastructure and keep it running at the standards our users expect. Importantly, this is a platform engineering team. Beyond operating infrastructure, the team is responsible for building the APIs, SDKs, and platform capabilities that enable AI, Data, and Engineering teams to safely and efficiently consume agent infrastructure as a service. Success in this role requires thinking beyond infrastructure operations and toward developer experience, platform adoption, and long-term scalability.

Requirements

• 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

Nice To Haves

• Experience building or operating infrastructure for agent-based or LLM-powered systems
• Familiarity with agent orchestration frameworks (e.g., LangGraph, CrewAI, or similar)
• Background in data infrastructure, including familiarity with Airflow, Kafka, Spark, or data lake tooling
• Experience with CI/CD pipelines and deployment automation for AI/ML workloads
• Exposure to evaluation frameworks and model performance monitoring at scale
• Experience working in fast-moving 0→1 environments or platform-building teams
• Experience building SDKs, developer tooling, or internal platform products with a strong focus on usability and adoption
• Experience with Cloudflare's cloud platform and product ecosystem, including networking, security, performance, and Zero Trust solutions

Responsibilities

• 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

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed