Site Reliability Engineer - AI Agents
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Key skills for this role
About the Role
Kraken seeks a Site Reliability Engineer to design, build, and operate the infrastructure layer supporting AI agent workflows in production. You will ensure reliability, scalability, and observability of agentic systems, and build platform services and APIs for engineering teams.
Key Skills for This Role
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 for engineering teams
- Manage and maintain compute, orchestration, and serving infrastructure for 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
- Build and maintain CI/CD pipelines for rapid, reliable deployment of AI services and agent workflows
- Define and implement guardrails, failure handling, and recovery patterns for 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
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
- 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
- High ownership mindset in a fast moving, high stakes production environment
Full Job Posting
Building the Future of Open Finance
- Payward, parent company of Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks, has spent 15 years building financial infrastructure.
- Kraken is one of the world's longest standing crypto platforms, trusted by over 10 million individuals and institutions globally.
- The AI Infrastructure team sits within the Data organization, building and scaling systems that power AI agents in production.
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 for engineering teams.
- Manage and maintain compute, orchestration, and serving infrastructure for 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.
- 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.
What You Bring
- 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.
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