> Markdown version of [/jobs/ext/1143431-senior-software-engineer-internally-deployed-products](https://www.wearedevelopers.com/jobs/ext/1143431-senior-software-engineer-internally-deployed-products). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# Senior Software Engineer, Internally Deployed Products

- **Company:** Mango Technologies, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $160,000.0 - $210,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Systems Engineering, User Authentication, Automation of Tests, BigQuery, Code Review, Extract Transform Load (ETL), Data Transformation, Cursor (Graphical User Interface Elements), Identity and Access Management, Python (Programming Language), OAuth, OpenID, Role-Based Access Control, Next.js, Salesforce.Com, Software Engineering, Systems Integration, TypeScript, Web Applications, AI Infrastructure, Data Logging, Scripting, Okta, GitHub Copilot, ReactJS, Large Language Models, Multi-Agent Systems, Prompt Engineering, Infrastructure Automation Frameworks, Low Latency, AWS Fargate, Hubspot, Cloudwatch, Api Gateway, Restful APIs, Terraform, Network Server, Data Pipelines, Dynatrace
- **Published:** July 1, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=7eb34b68319c8362

## About the Role

Do you have experience in TypeScript?, * 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, BigQuery, Cloud Functions) to complement AWS work
 * Salesforce, HubSpot, or other CRM API integration work - understanding GTM data models is a significant advantage
 * React or Next.js front-end capability to ship internal dashboards and tooling without hand-offs
 * Familiarity with agent orchestration frameworks: LangGraph, AutoGen, CrewAI, or custom orchestration patterns
 * Experience in a GTM Systems, RevOps, or Sales Engineering context
 * Okta / identity provider integration work (PKCE flows, SCIM, token management)

## Description

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., * 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, * Ownership - You own the full lifecycle of what you build: design, delivery, observability, and iteration. We don't have a separate ops hand-off.
 * Velocity - We ship to production in days, not quarters. AI tools are how we maintain that pace without sacrificing quality.
 * Atomicity - Every deliverable is a discrete, composable unit - an MCP server, an agent, an API. Nothing we build requires everything else to work.

Who Thrives Here

This role is a strong fit if you are genuinely excited to work at the intersection of AI infrastructure and internal product - not just as a user of AI, but as someone building the layer that makes AI useful for an entire go-to-market organization. You are comfortable with ambiguity, you default to shipping, and you see every piece of internal tooling as a product that deserves operational discipline.

This role is not a fit if you need heavily defined requirements before starting work, prefer long planning cycles to iterative delivery, or view AI tools as optional extras rather than core to how you operate.


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