Senior Software Engineer, Internally Deployed Products

ClickUp is an AI-native company building the first truly converged AI workspace. As the Senior Software Engineer, you will own the technical delivery of internal AI products, focusing on developing and maintaining the infrastructure that powers AI-first work across various functions.

Responsibilities:

• Design, build, and operate Model Context Protocol servers that expose CRM, ticketing, analytics, and communication data to AI agents across the GTM stack
• Implement Okta PKCE authentication flows and RBAC policy enforcement so agents access only the data they're authorized to touch
• Maintain deployment infrastructure on AWS (Bedrock, Lambda, ECS, API Gateway) and contribute to GCP workloads where applicable
• Own observability: structured logging, distributed tracing, latency SLOs, and on-call runbooks for every production server
• Build and maintain multi-step autonomous agents that execute end-to-end GTM workflows — lead qualification, deal room assembly, onboarding automation, support triage, and more
• Architect prompt engineering frameworks, tool-call schemas, and agent evaluation harnesses that make AI behavior predictable and auditable
• Integrate with LLM providers (Anthropic, OpenAI, AWS Bedrock AgentCore) and maintain version-pinned, cost-tracked model configurations
• Deliver AI-powered internal applications (web apps, CLI tools, Slack integrations) that non-technical GTM stakeholders use without friction
• Own full-stack feature delivery across TypeScript/Node.js backends and React/TypeScript frontends for internal tooling
• Write Python automation scripts, ETL pipelines, and data transformation layers that feed GTM analytics and AI context
• Collaborate with Systems Engineering and GTM Engineering teams on cross-cutting API standards, data contracts, and integration patterns
• Conduct code reviews, establish engineering standards, and actively mentor junior engineers toward higher leverage
• Use AI coding assistants (Claude, Cursor, GitHub Copilot) as primary engineering accelerators — not supplements — to ship at a pace that punches above a single engineer's weight
• Document AI usage patterns, prompt templates, and agentic workflows so the team's collective throughput compounds
• Stay current on MCP protocol evolution, agent frameworks (LangGraph, CrewAI, custom), and emerging LLM capabilities; bring back what matters