# 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