AI Engineer

Pelajari cara membangun, deploy, dan maintain production-ready AI applications dengan best practices industry-standard.

Apa itu AI Engineer?

AI Engineer adalah role yang menggabungkan:

Software Engineering fundamentals
Machine Learning knowledge
LLM application development
Production system design
DevOps & MLOps practices

Apa yang Akan Dipelajari

Architecture Design

Merancang scalable dan maintainable AI application architecture.

LLM Integration

Integrating LLMs dengan existing systems dan workflows.

Production Deployment

Deploy AI applications ke production dengan proper monitoring.

Optimization & Scaling

Optimize performance dan scale untuk high traffic.

Full-Stack AI Architecture

User Interface Layer

Technologies:

React / Next.js
TypeScript
Tailwind CSS
Streaming responses
Real-time updates

Key Features:

Chat interfaces
File uploads
Progress indicators
Error handling
Responsive design

API & Business Logic

Technologies:

Node.js / Python
REST / GraphQL
WebSockets
Authentication (JWT, OAuth)
Rate limiting

Responsibilities:

Request validation
Business logic
Data processing
API orchestration
Security enforcement

LLM & AI Services

Components:

LLM providers (OpenAI, Anthropic)
Vector databases (Pinecone, Weaviate)
Embedding models
Agent frameworks
Tool integrations

Patterns:

RAG (Retrieval Augmented Generation)
Fine-tuning
Prompt caching
Function calling

Deployment & Operations

Stack:

Cloud providers (AWS, GCP, Azure)
Container orchestration (Docker, Kubernetes)
CI/CD pipelines
Monitoring (Datadog, Sentry)
Logging (ELK stack)

Concerns:

Scalability
Reliability
Security
Cost optimization

Core Patterns & Techniques

Production Considerations

Security

Security Checklist

Input Validation: Sanitize all user inputs
Output Filtering: Filter sensitive information
API Key Management: Use secrets management (Vault, AWS Secrets)
Rate Limiting: Prevent abuse dan control costs
Authentication: Implement proper auth (JWT, OAuth)
Audit Logging: Track all AI interactions
Prompt Injection Protection: Validate dan sanitize prompts

Cost Optimization

Choose Right Model for Task

GPT-4: Complex reasoning, high accuracy
GPT-3.5: Fast, cost-effective, simple tasks
Claude: Long context, analysis
Llama: Self-hosted, privacy

Strategy: Use cheaper models untuk simple tasks, expensive untuk complex

Implement Multi-Layer Caching

Application cache: Redis, Memcached
Semantic cache: Vector similarity
CDN cache: Static responses

Impact: 70-90% cost reduction

Batch Requests When Possible

Combine multiple queries
Process in parallel
Reduce API calls

Example: Batch embeddings generation

Track Usage & Costs

Monitor token usage
Set budget alerts
Analyze cost per feature
Optimize expensive queries

Tools: OpenAI usage dashboard, custom analytics

Performance Optimization

Monitoring & Observability

Key Metrics

Performance Metrics

Latency, throughput, error rates, token usage

Cost Metrics

API costs, infrastructure costs, cost per user

Quality Metrics

Response accuracy, user satisfaction, task completion

Business Metrics

User engagement, retention, conversion rates

Monitoring Stack

// Example: Logging with structured data
import { logger } from './logger';

logger.info('LLM request', {
  model: 'gpt-4',
  tokens: 1500,
  latency: 2.3,
  cost: 0.045,
  userId: user.id,
  success: true
});

// Example: Error tracking
import * as Sentry from '@sentry/node';

try {
  const response = await llm.generate(prompt);
} catch (error) {
  Sentry.captureException(error, {
    tags: {
      component: 'llm',
      model: 'gpt-4'
    },
    extra: {
      prompt: prompt.substring(0, 100),
      userId: user.id
    }
  });
}