AI Engineer
Software engineers who master LLMs. They build AI applications and ship features.
Product-minded. Design-aware. Full-stack. Focused on business outcomes.
🎯 Responsibilities
Application Development
Build AI apps using foundation models
Create LLM interfaces
Integrate AI into products via APIs
Prompt Engineering
Write effective prompts
Build context systems
Manage memory for conversations
Evaluation Systems
Select models
Benchmark progress
Detect production issues
Build evaluation frameworks
Infrastructure Operations
Optimise inference speed and cost
Manage model serving
Monitor performance
Handle compute clusters
Data Engineering
Curate datasets
Process unstructured data
Implement retrieval systems
Control data quality
🛠️ Essential Skills
AI-Specific Knowledge
Prompt engineering techniques
Model selection strategies
Model evaluation methold
Model fine-tuning approaches
Product Development
UX development for AI interfaces
Experimentation workflows
Business outcome measurement
Problem-solving with AI
➡️ ML Engineer to AI Engineer
From Model Training to Orchestration
Before: Train models from scratch
After: Orchestrate existing foundation models
From Data-First to Product-First
Before: Collect data, then build
After: Build fast, validate, then optimise
From Research to Application
Before: Design algorithms
After: Build user experiences
➡️ Product Engineer to AI Engineer
AI-Specific Knowledge
Learn: LLM capabilities and limits
Develop: Prompt engineering skills
Understand: Model selection
Data Handling Skills
Expand: Unstructured data processing
Master: Context retrieval
Build: Data quality workflows
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