Senior Software Engineer, Internally Deployed Products

At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀

The Mission

The Foundry is ClickUp's internal AI innovation lab — embedded inside GTM Systems and accountable for turning AI capabilities into production-grade, internally deployed products that make every GTM function faster and smarter. We build the infrastructure that powers AI-first work across Sales, Marketing, Post-Sales, and Revenue Operations.

As the Senior Software Engineer on this team you will own the technical delivery of our MCP server platform, agent orchestration layer, and internal tooling — shipping production systems used daily by hundreds of ClickUp employees, and scaling your own throughput by treating AI tools as first-class engineering collaborators.

What You'll Own

MCP Server Platform

• 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

Agent Orchestration & AI-Native Products

• 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

GTM Platform Engineering

• 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

AI-Native Development Practice

• 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

Required Qualifications

• 5+ years of professional software engineering experience with production systems

• Expert-level TypeScript and Node.js — idiomatic, typed, testable server-side code

• Strong Python — automation scripts, data pipelines, and scripting for AI/ML tooling

• Meaningful AWS deployment experience: Lambda, Bedrock, ECS/Fargate, API Gateway, IAM, Secrets Manager, CloudWatch

• Demonstrated experience integrating with LLM APIs (OpenAI, Anthropic, AWS Bedrock, or equivalent) and shipping AI-powered features to real users

• Solid foundation in REST API design, OAuth 2.0 / OIDC authentication, and secure credential management

• Experience with CI/CD pipelines, infrastructure-as-code (Terraform, CDK, or SAM), and cloud cost awareness

• Clear written communication: design docs, ADRs, and runbooks that others actually read

• Track record of using AI tools (LLM assistants, copilots, agentic workflows) as a genuine productivity multiplier — not a gimmick

Strongly Preferred

• Hands-on experience with MCP (Model Context Protocol) — building servers, defining tool schemas, or operating multi-server agent environments

• GCP experience (Cloud Run,