Applied AI Engineer

Design, implement, and continuously refine system prompts, character behavior definitions, and guardrails for our AI character platform.

Build evaluation frameworks to measure character fidelity, response quality, and safety — establishing quantitative standards for what "in character" actually means across interaction types.

Architect and implement RAG systems to ground character responses in canonical knowledge, including embedding strategies, vector retrieval, and relevance evaluation.

Partner with creative and brand teams to translate character attributes — personality, voice, canon, tone, and boundaries — into precise, testable AI behavior and system requirements.

Lead prompt versioning, regression testing, and model evaluation practices to ensure character behavior remains consistent across provider updates and model changes.

Integrate with multi-provider LLM APIs (OpenAI, Google Gemini, etc.) and make principled selection decisions as the landscape evolves.

Evaluate and adopt emerging techniques in prompt engineering, agentic design, and LLM evaluation that meaningfully advance the quality and reliability of character experiences.

3+ years of experience building and shipping production LLM-powered systems.

Deep expertise in prompt engineering — system prompt design, few-shot techniques, chain-of-thought, multi-turn conversation management, and structured output.

Experience designing and implementing evaluation frameworks for LLM outputs, including adversarial and security testing.

Hands-on experience with RAG architectures: embedding models, vector stores, chunking strategies, and retrieval evaluation.

Proficiency with major LLM APIs (OpenAI, Google Gemini, Anthropic, or equivalent) and experience making principled decisions between them.

Strong Python skills and comfort working in AWS environments.