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