Ref - Rise of an AI Engineer

Who is an AI Engineer?

AI Engineers are experts in crafting, developing, and deploying AI systems, playing a crucial role across diverse industries. They design applications that empower machines to execute tasks traditionally requiring human intelligence, including:

• Problem-solving and decision-making
• Natural language understanding and generation
• Knowledge integration and retrieval
• Autonomous agent development

Core Competencies

AI Engineers need to master several key customization strategies:

1. Prompt Engineering

   • Crafting effective instructions and context
   • Implementing zero-shot, one-shot, and few-shot learning
   • Developing chain-of-thought and tree-of-thoughts approaches

2. Model Selection & Optimization

   • Choosing between open-source and proprietary models
   • Understanding model architectures and capabilities
   • Implementing decoding and sampling strategies

3. Knowledge Integration

   • Building RAG (Retrieval Augmented Generation) systems
   • Managing vector databases and embeddings
   • Implementing efficient retrieval strategies

4. Agent Development

   • Creating autonomous AI agents
   • Implementing ReAct patterns
   • Developing multi-agent systems

5. Model Customization

   • Fine-tuning pre-trained models
   • Implementing PEFT (Parameter Efficient Fine Tuning)
   • Applying RLHF (Reinforcement Learning from Human Feedback)

Difference between AI Engineer vs ML Engineer

AI Engineer

• Leverages pre-trained models and existing AI tools
• Focuses on practical applications and system integration
• Specializes in prompt engineering and model customization
• Builds AI-powered applications and workflows

ML Engineer

• Develops new models from scratch
• Focuses on model architecture and training
• Specializes in data pipeline and model optimization
• Builds and maintains ML infrastructure

Core Concepts & Principles

Implementation Areas

Best Practices

1. Model Selection

   • Evaluate task-specific requirements
   • Consider computational resources
   • Balance cost vs performance

2. System Design

   • Implement robust authorization
   • Design for scalability
   • Ensure security and privacy

3. Development

   • Follow AI safety guidelines
   • Implement monitoring and logging
   • Maintain version control

References

• Rise of an AI Engineer
• AI Engineer - Next Big Tech Role
• Common LLM Customization Strategies