Azure AI Foundry Resume Evaluation System Setup Guide

1. Azure AI Foundry Initial Setup

1-1. Azure Portal Access

1. Access https://ai.azure.com/
2. Sign in with Microsoft account (work or personal account)
3. Start free trial if you don't have an Azure subscription

1-2. Project Creation

1. Click + New project on the home screen
2. Enter project name (e.g., "resume-evaluation-project")
3. Select or create a new Resource group
4. Select region (Korea Central recommended)
5. Click Create

1-3. Model Deployment

1. Click Deployments in the left menu
2. Click + Deploy model
3. Select model:
   - Recommended: gpt-4o-mini (cheaper and faster)
   - Alternative: gpt-4 (more accurate but expensive)
4. Enter Deployment name (e.g., "gpt-4o-mini")
5. Click Deploy
6. Wait for deployment completion (approximately 1-2 minutes)

1-4. Connection Information Verification

From Deployments page:
- Copy Deployment name → 'deployment_name' in code
- Copy Target URI → 'azure_endpoint' in code
Example: https://your-resource-name.openai.azure.com/

From Keys and Endpoint page:
- Click Keys and Endpoint in the left menu
- Copy Key 1 or Key 2 → 'api_key' in code

2. Python Environment Setup

내부 정보 생략 :

3. Execution Method

내부 정보 생략 :

4. Expected Results

Output on Successful Execution:

✓ Azure AI Foundry connection successful (API Key)
  Endpoint: https://your-resource.openai.azure.com/
  Deployed Model: gpt-4o-mini
✓ MariaDB connection successful

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Azure AI Resume Evaluation Started (Team ID: 1)
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 [ppl-ai-file-upload.s3.amazonaws](https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/attachments/58947564/ed93b713-4066-4878-a7f4-375a76f72be6/Azure_AI_Foundry_seoljeonggaideu.md) Team: Platform_Build_Team

[2] Analyzing skills with Azure AI...
✓ Azure AI analysis complete (Matched skills: 7)
  Experience: 5.0 years
  Matched skills: 7
  AI Provider: Azure OpenAI
  Model: gpt-4o-mini
  Strengths: Terraform expertise, Kubernetes operation experience

[3] Calculating scores...

[4] Qualitative evaluation (Azure AI)...

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Evaluation Results
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Technical Score: 85.3/100
Experience Score: 90.0/100
Qualitative Score: 75.0/100

Final Total Score: 83.6/100
Grade: B+

✓ Strengths:
  • Terraform expertise
  • Kubernetes operation experience
  • AWS cloud hands-on experience

Overall Assessment: A competent infrastructure engineer with strengths in IaC and container technologies.