AI Platform QA Tester Required
Budget: $15 – $25 USD
QA Tester Needed for AI Integration Platform Testing
Project Type: Quality Assurance Testing
Duration: Short-term project
Experience Level: Intermediate
Job Description
I'm looking for a detail-oriented QA tester to help test AI integrations on our platform. This is a structured testing role that involves systematically evaluating how AI models interact with popular business tools through our integration platform.
What You'll Be Doing
Setup & Configuration:
Authenticate and configure MCP (Model Context Protocol) servers for multiple integrations including: Gmail, Slack, Notion, GitHub, Jira, Linear, Google Calendar, ClickUp, and Asana
Connect these integrations to OpenAI models through our unified platform
Set up test data in external systems to create realistic testing scenarios
Testing Execution:
Execute 100 predefined user queries across different integrations
Test various scenarios including read, write, update, and delete operations
Document results systematically in a provided Google Spreadsheet
Documentation Requirements:
Rate each query outcome (correct/incorrect/partially correct)
Capture evidence screenshots from external systems
Export and record complete AI interaction traces
Identify critical tool calls that fulfill user requests
Requirements
Must Have:
Experience with QA testing methodologies
Active accounts on: Gmail, Slack, Notion, GitHub, Jira, Linear, Google Calendar, ClickUp, and Asana
Strong attention to detail and systematic approach
Ability to follow detailed technical instructions
Experience with API testing and JSON trace analysis
Nice to Have:
Familiarity with AI/ML platforms
Experience with integration testing
Knowledge of OpenAI Playground
What I'll Provide
API key for the platform
Access to OpenAI project (no LLM costs for you)
Detailed testing instructions and procedures
Pre-formatted Google Spreadsheet for results
Complete list of 100 test queries
Deliverables
Completed Google Spreadsheet with all 100 query results
Evidence screenshots for each test case
JSON traces of AI interactions
Summary report of any issues or patterns discovered
Project Type: Quality Assurance Testing
Duration: Short-term project
Experience Level: Intermediate
Job Description
I'm looking for a detail-oriented QA tester to help test AI integrations on our platform. This is a structured testing role that involves systematically evaluating how AI models interact with popular business tools through our integration platform.
What You'll Be Doing
Setup & Configuration:
Authenticate and configure MCP (Model Context Protocol) servers for multiple integrations including: Gmail, Slack, Notion, GitHub, Jira, Linear, Google Calendar, ClickUp, and Asana
Connect these integrations to OpenAI models through our unified platform
Set up test data in external systems to create realistic testing scenarios
Testing Execution:
Execute 100 predefined user queries across different integrations
Test various scenarios including read, write, update, and delete operations
Document results systematically in a provided Google Spreadsheet
Documentation Requirements:
Rate each query outcome (correct/incorrect/partially correct)
Capture evidence screenshots from external systems
Export and record complete AI interaction traces
Identify critical tool calls that fulfill user requests
Requirements
Must Have:
Experience with QA testing methodologies
Active accounts on: Gmail, Slack, Notion, GitHub, Jira, Linear, Google Calendar, ClickUp, and Asana
Strong attention to detail and systematic approach
Ability to follow detailed technical instructions
Experience with API testing and JSON trace analysis
Nice to Have:
Familiarity with AI/ML platforms
Experience with integration testing
Knowledge of OpenAI Playground
What I'll Provide
API key for the platform
Access to OpenAI project (no LLM costs for you)
Detailed testing instructions and procedures
Pre-formatted Google Spreadsheet for results
Complete list of 100 test queries
Deliverables
Completed Google Spreadsheet with all 100 query results
Evidence screenshots for each test case
JSON traces of AI interactions
Summary report of any issues or patterns discovered
Related categories:
Python
Testing / QA
Software Testing
Test Automation
JSON
AI (Artificial Intelligence) HW/SW
API Testing
OpenAI