Automation & Applied AI Engineer – AI-Driven Workflows

United States Digital Space LLC

United States

Remote

USD 120,000 - 160,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

Medical, Dental, Vision plans

STD and Basic Life/AD&D

Job summary

Teachers of Tomorrow, based in Houston, TX, seeks an Automation & Applied AI Engineer to design and implement automation workflows and AI integrations. This hands-on role focuses on practical delivery, not enterprise architecture, working closely with IT leadership and business stakeholders to turn requirements into reliable automation solutions.

The ideal candidate has 4–7 years in automation, experience with Power Platform/Azure Logic Apps, REST/JSON familiarity, and exposure to LLM-based

Qualifications

4-7 years of experience in automation, systems integration, or process improvement.
Hands-on experience with Microsoft Power Platform and/or Azure Logic Apps.
Experience with REST APIs and JSON.
Exposure to LLM-based systems (Azure AI Factory, OpenAI, or similar).
Experience implementing function calling or tool-based AI integrations.
Strong troubleshooting and debugging skills.
Ability to work independently and own deliverables.

Responsibilities

Build and maintain automation workflows using platforms similar to Power Platform/Azure Logic Apps.
Improve and refactor existing workflows for reliability and maintainability.
Troubleshoot automation issues in production environments.
Ensure proper error handling and logging within automated processes.
Implement conversational AI systems and tool integration.
Integrate chatbot workflows with internal systems via REST APIs.
Define structured outputs and JSON schemas for reliable AI-to-system communication.
Assist in building MCP servers or similar middleware to extend AI capabilities.
Connect systems using REST APIs, webhooks, and JSON payloads.
Transform and validate data across CRM, marketing, and internal systems.
Support reporting and monitoring of automated processes.