Site Reliability Engineer - AI Agents

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Building the Future of Open Finance

Payward - 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 team

Founded 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 opportunity

• 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