LLM Engineer Needed to Build & Stabilize ChatGPT Consumer Insights Agent (OpenAI API)
I’ve developed a sophisticated prototype inside ChatGPT Business that functions as a Consumer Insights Strategist — an AI agent designed to analyze qualitative data, synthesize insights, and generate strategic narratives. The concept is working well, but it’s now hitting ChatGPT’s instruction and context limits (8K character cap) and starting to show instability — fixing one behavior often breaks another. I need an AI engineer / LLM systems developer who can help me translate this working prototype into a stable, scalable structure using the OpenAI API (or a similar high-context model). What I Need Evaluate my current prototype logic (I’ll share files like Instructions_v24, Field_Guide, etc.). Rebuild the system using modular prompts, retrieval (RAG), or LangChain-style orchestration so the logic is divided into stable components. Preserve the personality, voice, and reasoning structure of the agent exactly as designed. Ensure it runs reliably without “forgetting” instructions or drifting off-topic during long sessions. Advise on the right setup for memory, context management, and long-term scalability. Ideal Background Deep experience with OpenAI API (GPT-4-Turbo, GPT-5, Assistants API, or similar) Strong prompt engineering and LLM architecture skills Familiarity with LangChain, LlamaIndex, or custom retrieval pipelines Understanding of context/token budgeting and modular prompt design (Bonus) Experience working on AI tools for strategy, insights, or qualitative research Apply tot his job