Develop ready-made skills — reusable components for common AI tasks (text analysis, classification, RAG, agentic workflows, etc.)
Optimize prompts across different models and scenarios; conduct A/B testing and measure output quality
Design RAG integrations and agentic pipelines — from architecture to implementation
Work with integrations — connecting external APIs, vector databases, knowledge sources
Test and document skills — create usage examples, handle edge cases, establish best practices
Analyze requirements and turn them into scalable components
Monitor skill performance and iterate based on feedback
Stay on top of AI trends and add new capabilities to the platform

Technical education (completed or in progress) — CS, Engineering, Mathematics, Physics, or related field
Deep understanding of LLMs — how modern models work, their limitations and strengths; hands-on experience with ChatGPT, Claude, Gemini beyond casual use
Prompt engineering experience — not just writing prompts, but optimizing them, A/B testing, understanding how different parameters affect outputs
Python development — at a level sufficient for creating integrations, testing, and automation
Systematic and architectural thinking — ability to design scalable solutions and anticipate edge cases
Analytical approach — ability to measure, compare, and make decisions based on data
English B1+ — reading docs, API documentation, research papers
Self-sufficiency — doesn't require step-by-step instructions; can navigate unfamiliar systems independently

AI Skill Engineer

Key skills