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Bandwidth

Internal Applications Engineer

Raleigh, NCmidAdded today

About this role

Bandwidth seeks an Internal Applications Engineer to design and deploy automation workflows, API integrations, and AI-powered internal tools that support company operations. You'll work across workflow orchestration, system integrations, data pipelines, and user-facing applications using a modern tech stack including Python, FastAPI, React, and generative AI platforms.

What you'll do

  • Identify and implement automation opportunities using generative AI and workflow orchestration platforms to reduce manual effort
  • Develop internal tools and applications using React and FastAPI deployed on container infrastructure
  • Configure and maintain integrations between operational platforms like HubSpot and ServiceNow using n8n, Windmill, and Python
  • Build and maintain data pipelines that flow operational data into Snowflake
  • Develop AI-powered solutions leveraging LLM APIs from Anthropic, OpenAI, and others
  • Write Python and JavaScript scripts to automate tasks and extend automation capabilities across the team

What they're looking for

  • Python and JavaScript development in production environments
  • REST API integration and design
  • Workflow automation platforms (n8n, Windmill)
  • AI development tools (Claude Code, Cursor, Copilot)
  • Automated testing frameworks (Pytest, Vitest)
  • Docker containerization and AWS cloud platforms
  • FastAPI and React frontend development
  • LLM API integration (OpenAI, Anthropic)
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Bandwidth

Bandwidth builds next-generation communications products powered by AI and intelligent automation technologies. The company is hiring NetSuite developers and AI engineers to design automation solutions, prototype advanced AI/ML systems, and develop cutting-edge communications capabilities.

Website
bandwidth.com
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Likely interview questions

  • Describe a complex API integration you've built—how did you handle authentication and data transformation?
  • How do you approach writing tests, and can you give an example of how AI coding tools have changed your testing workflow?
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