Site Reliability Engineer - AI Agents

About the role

Building the Future of Open FinancePayward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system.Before you apply, we encourage you to explore our culture page to understand what drives us and how we work.The teamFounded in 2011, Kraken is one of the world s longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.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.The opportunityDesign, build, and operate the infrastructure layer supporting AI agent workflows in productionEnsure reliability, scalability, and observability of agentic systems across internal and external productsDesign and develop platform services, APIs, SDKs, and self-service capabilities that allow engineering teams to easily consume AI infrastructure and agent platform servicesManage and maintain the compute, orchestration, and serving infrastructure powering model inference and agent executionImplement robust monitoring, alerting, and incident response procedures tailored to AI/ML workloadsUtilize Infrastructure as Code (IaC) tools such as Terraform to provision and manage cloud (AWS) infrastructure componentsBuild and maintain CI/CD pipelines that support rapid, reliable deployment of AI services and agent workflowsDefine and implement guardrails, failure handling, and recovery patterns specific to agentic and LLM-powered systemsCollaborate with AI and Data Engineering teams to translate experimental agent prototypes into hardened production systemsManage containerized workloads using Kubernetes, ensuring efficient deployment, scaling, and orchestration of AI servicesImplement access controls and security best practices across AI infrastructure environmentsDocument architecture, runbooks, and best practices to support knowledge sharing across the teamWhat You Bring5+ years of experience as a Site Reliability Engineer, Infrastructure Engineer, Platform Engineer, or similar role in a production environmentHands-on experience supporting ML infrastructure, model serving, or MLOps workflows in productionExperience building developer platforms, internal tooling, APIs, or SDKs consumed by engineering teams at scaleStrong understanding of platform engineering principles, including developer experience, self-service infrastructure, and API-driven platform designProficiency with Infrastructure as Code tools, particularly TerraformExperience with containerization and orchestration, particularly Kubernetes and DockerSolid understanding of cloud infrastructure, preferably AWSStrong scripting skills (bash/shell) and proficiency in at least one programming language (Python preferred)Experience designing and operating observability, monitoring, and alerting systemsExperience implementing incident response procedures and participating in on-call rotationsStrong collaboration skills working across data, AI, and engineering teamsHigh ownership mindset in a fast-moving, high-stakes production environmentNice to havesExperience building or operating infrastructure for agent-based or LLM-powered systemsFamiliarity with agent orchestration frameworks (e.g., LangGraph, CrewAI, or similar)Background in data infrastructure, including familiarity with Airflow, Kafka, Spark, or data lake toolingExperience with CI/CD pipelines and deployment automation for AI/ML workloadsExposure to evaluation frameworks and model performance monitoring at scaleExperience working in fast-moving 0→1 environments or platform-building teamsExper

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