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

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