AI Engineering: The Emerging Discipline

ai_engineering:
  definition: Building applications that leverage AI models effectively

  distinct_from:
    ml_engineering:
      focus: Training and deploying models
      skills: PyTorch, model architecture, training infra
      output: Models

    software_engineering:
      focus: Building applications
      skills: Traditional programming, systems
      output: Applications

    ai_engineering:
      focus: Integrating AI into applications
      skills: Prompt engineering, RAG, evaluation
      output: AI-powered features

ai_engineer_skills:
  prompt_engineering:
    - Designing effective prompts
    - Few-shot and chain-of-thought
    - Prompt optimization and testing
    - Prompt management systems

  retrieval_systems:
    - RAG architecture
    - Vector databases
    - Chunking strategies
    - Hybrid search

  llm_integration:
    - API integration patterns
    - Error handling
    - Rate limiting
    - Cost optimization

  evaluation:
    - Quality metrics
    - A/B testing
    - Regression detection
    - User feedback loops

  safety_and_reliability:
    - Guardrails implementation
    - Content filtering
    - Hallucination mitigation
    - Failure handling

ai_engineering_stack:
  model_layer:
    - Model selection
    - API integration
    - Multi-model routing
    - Fine-tuning (when needed)

  data_layer:
    - Document processing
    - Embedding generation
    - Vector storage
    - Retrieval optimization

  application_layer:
    - Prompt management
    - Response handling
    - Caching strategies
    - User experience

  observability_layer:
    - Quality monitoring
    - Cost tracking
    - Latency metrics
    - Error analysis

  safety_layer:
    - Input validation
    - Output filtering
    - Content moderation
    - Compliance checks

learning_path:
  foundations:
    - How LLMs work (conceptually)
    - API usage and integration
    - Basic prompt engineering
    - Simple RAG implementation

  intermediate:
    - Advanced prompting techniques
    - Vector database expertise
    - Evaluation frameworks
    - Multi-model strategies

  advanced:
    - Agent architectures
    - Production reliability
    - Cost optimization at scale
    - AI safety practices

  specialized:
    - Fine-tuning techniques
    - Multi-modal applications
    - Domain-specific AI
    - AI infrastructure

skill_building_projects:
  beginner:
    - Chatbot with context
    - Document Q&A system
    - Content summarizer

  intermediate:
    - RAG with hybrid search
    - Multi-step workflow
    - Evaluation pipeline

  advanced:
    - Production agent system
    - Multi-modal application
    - Enterprise-grade AI feature

ai_engineering_roles:
  individual_contributor:
    junior:
      - Basic AI feature implementation
      - Prompt development
      - Integration work

    senior:
      - System design
      - Production reliability
      - Complex features
      - Mentoring

    staff:
      - Architecture
      - Cross-team patterns
      - Technology strategy

  leadership:
    - AI engineering manager
    - Director of AI
    - VP of AI Engineering

team_interactions:
  with_product:
    - Feature feasibility
    - Quality expectations
    - User experience design

  with_ml:
    - Model selection
    - Fine-tuning needs
    - Evaluation metrics

  with_platform:
    - Infrastructure needs
    - Observability
    - Reliability requirements

  with_security:
    - Safety requirements
    - Compliance needs
    - Risk assessment

market_demand:
  job_postings:
    - "AI Engineer" roles emerging
    - Distinct from "ML Engineer"
    - Focus on LLM integration

  skill_requirements:
    common:
      - LLM API experience
      - RAG implementation
      - Prompt engineering
      - Python proficiency

    differentiated:
      - Production AI experience
      - Evaluation expertise
      - Safety knowledge

  compensation:
    trend: Premium over traditional SWE
    range: Varies by experience and location