Senior DevOps Engineer

Responsibilities

• Support and evolve the infrastructure behind URBN's ecommerce sites, ensuring high availability, performance, and resilience during peak traffic events like launches and promotional campaigns
• Operate and improve the infrastructure supporting our Order Management System (OMS) and the integration layer connecting commerce, fulfillment, ERP, and third-party services
• Design and maintain deployment pipelines supporting Java, Node, Python, and more with strong standards around testing, rollback, and release safety
• Build the tooling and abstractions that let engineering teams across URBN's brands self‑serve infrastructure, standardize deployments, and ship faster without sacrificing reliability
• Architect and operate the platform layer for URBN's enterprise AI initiatives including LLM orchestration, MCP server deployments, and agent execution runtimes with an eye toward security and cost efficiency
• Partner with product and data teams to build infrastructure that makes AI capabilities accessible across the organization: RAG pipelines, vector search, embedding services, and API gateways for AI tools
• Own the observability stack across commerce and AI systems including distributed tracing, metrics, alerting, and on‑call support so teams can detect and resolve issues quickly at any layer
• Implement and maintain controls around access governance, secrets management, PII handling, and audit logging across both commerce-critical and AI workloads
• Monitor and optimize cloud spend across ecommerce infrastructure, integration services, and AI inference using autoscaling, rightsizing, and intelligent traffic routing to maximize efficiency

• 5+ years in Dev Ops, Platform Engineering, or SRE roles
• Deep expertise with Kubernetes (EKS, GKE, or AKS) in production
• Infrastructure‑as‑Code:
  Terraform
• Cloud‑native architecture on AWS, GCP, or Azure
• CI/CD tooling:
  Git Hub Actions, ArgoCD, Tekton, or equivalent
• Strong scripting/automation in Python or Go
• Observability stack experience:
  Open Telemetry, Prometheus, Grafana, or New Relic
• Demonstrated experience shipping and operating production ML or AI systems
• Hands‑on experience with LLM APIs (Anthropic, OpenAI, or AWS Bedrock) preferred
• MCP (Model Context Protocol) server development or deployment experience preferred
• Vector database operations:
  Pinecone, Weaviate, Qdrant, or pgvector preferred
• Experience building platforms at enterprise scale (1,000+ internal users) preferred
• Developer experience (DX) engineering background preferred
• Security certifications: CKS, CISSP, CCSP are a plus