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🤖 AI Engineer - Complete Guide

Specialized guide for AI Engineers at Appgain

🎯 Role Overview

AI Engineers at Appgain focus on implementing and maintaining local AI infrastructure, developing generative AI applications, and ensuring privacy-compliant AI solutions.

📚 Specialized Learning

Required Courses

Additional Resources

🛠️ AI Infrastructure

Local LLAMA Setup

  • Server: http://ovh-n8n:11434
  • Models Available:
  • CodeLlama: 7B parameters (Code generation)
  • Llama3.2: 3.2B parameters (General purpose)
  • Mistral: 7.2B parameters (High performance)
  • Gemma2: 2.6B parameters (Google's model)

n8n Workflow Automation Server

  • URL: https://n8n.instabackend.io/
  • Purpose: Workflow automation and data integration
  • Access: Available for AI, Support, and Data Engineers
  • Features:
  • Workflow Automation: Create automated data processing workflows
  • Data Integration: Connect various data sources and APIs
  • AI Pipeline Integration: Integrate AI models into data workflows
  • Real-time Processing: Stream data through AI models
  • Custom Triggers: Set up automated triggers for AI processing

Setup Guide

  • Documentation: Installing LLAMA with GUI
  • Server Access: ovh-growthmachine (AI/ML server)
  • Environment: Docker containers with GPU support

🔧 Key Responsibilities

1. AI Model Management

  • Model Deployment: Deploy and maintain local AI models
  • Performance Optimization: Monitor and optimize model performance
  • Model Updates: Regular model updates and version management
  • Resource Management: GPU memory and computational resource allocation

2. Privacy & Security

  • Data Privacy: Ensure all processing stays local
  • Security Audits: Regular security assessments of AI systems
  • Access Control: Manage access to AI infrastructure
  • Compliance: Ensure AI systems meet privacy regulations

3. Application Development

  • API Development: Create APIs for AI model access
  • Integration: Integrate AI capabilities into existing applications
  • Testing: Comprehensive testing of AI functionality
  • Documentation: Maintain AI system documentation

🚀 Technical Stack

Core Technologies

  • Python: Primary development language
  • Ollama: Local model serving
  • LangChain: AI application framework
  • Docker: Containerization for AI models
  • GPU Computing: NVIDIA GPUs for model inference

Development Tools

  • Jupyter Notebooks: Model experimentation and testing
  • Git: Version control for AI models and code
  • Postman: API testing for AI endpoints
  • Monitoring: Prometheus/Grafana for AI metrics

📊 Success Metrics

Performance Metrics

  • Response Time: < 2 seconds for model inference
  • Accuracy: > 90% for specific use cases
  • Uptime: 99.9% AI service availability
  • Resource Utilization: < 80% GPU utilization

Privacy Metrics

  • Data Localization: 100% local processing
  • No External Calls: Zero API calls to external services
  • Cost Efficiency: Zero API fees
  • Offline Capability: Full offline functionality

🔗 Integration Points

System Integration

  • Appgain Server: AI capabilities integration
  • Notify Service: AI-powered notification content
  • Automator: AI-driven workflow automation
  • Admin Server: AI management interface

Data Sources

  • MongoDB: User data and preferences
  • PostgreSQL: Analytics and reporting data
  • Redis: Caching for AI responses
  • External APIs: Data enrichment (privacy-compliant)

📋 Daily Operations

Morning Routine

# Check AI model status
curl http://ovh-airbyte:11434/api/tags

# Monitor GPU usage
nvidia-smi

# Check model performance
python scripts/ai_health_check.py

Development Workflow

# Start development environment
docker compose -f ai-stack/docker-compose.yml up -d

# Test model inference
python scripts/test_model.py --model codellama

# Update model
ollama pull codellama:latest

# Deploy changes
bash scripts/deploy_ai.sh

Monitoring & Maintenance

# Monitor AI metrics
curl http://monitor.instabackend.io:9090/a../query?query=ai_response_time

# Check logs
docker logs ai-model-server

# Backup models
bash scripts/backup_models.sh

🎯 Project Examples

1. Smart Content Generation

  • Goal: Generate personalized notification content
  • Technology: LLAMA + LangChain
  • Integration: Notify Service
  • Metrics: Content relevance, engagement rates

2. Automated Customer Support

  • Goal: AI-powered customer service responses
  • Technology: Mistral + Custom training
  • Integration: Admin Server
  • Metrics: Response accuracy, customer satisfaction

3. Data Analysis Automation

  • Goal: Automated insights from user data
  • Technology: Gemma2 + Analytics pipeline
  • Integration: Growth Machine
  • Metrics: Insight quality, analysis speed

🔧 Troubleshooting

Common Issues

  • Model Loading: Check GPU memory availability
  • Performance: Monitor resource utilization
  • API Errors: Verify model server status
  • Integration Issues: Check API endpoints and authentication

Debug Commands

# Check model status
ollama list

# Test model inference
ollama run codellama "Hello, how are you?"

# Monitor system resources
htop

# Check API endpoints
curl -X POST http://ovh-airbyte:11434/api/generate

📚 Learning Path

Week 1: Foundation

  • Complete AI foundation courses
  • Set up local development environment
  • Understand AI infrastructure architecture
  • Learn Ollama and LangChain basics

Week 2: Hands-on

  • Deploy first AI model
  • Create simple AI API
  • Integrate with existing systems
  • Learn monitoring and metrics

Week 3: Advanced

  • Optimize model performance
  • Implement privacy controls
  • Create AI-powered features
  • Document AI systems

Week 4: Production

  • Deploy to production
  • Monitor and maintain
  • Optimize for scale
  • Share knowledge with team

🎥 Video Resources & Tutorials

AI & Automation Videos

Automator Journey Builder

🎯 Quick Navigation

🤖 AI Engineers are at the forefront of our AI initiatives, ensuring privacy-compliant, high-performance AI solutions that enhance our platform capabilities.

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