Prompt Engineer & LLM Systems Designer

Role Overview NeuralCraft AI is building the next generation of enterprise AI products and we need a Prompt Engineer & LLM Systems Designer who can sit at the intersection of language, logic, and product. You won't be training models; you'll be crafting the instruction layers, reasoning pipelines, and evaluation systems that make our AI reliably useful in high-stakes domains like legal, finance, and healthcare. What You Will Do Design and iterate on prompt pipelines for multi-step reasoning tasks across legal, finance, and healthcare verticals. Build and maintain RAG (Retrieval-Augmented Generation) systems using LangChain, LlamaIndex, and vector databases such as Pinecone or Weaviate. Architect prompt chains for agentic LLM workflows involving tool use, memory, and conditional branching. Define and run structured evaluation frameworks to measure output quality, hallucination rates, and task completion. Collaborate with ML engineers on fine-tuning strategies for domain-specific model adaptation. Document prompt templates, versioning strategies, and evaluation results for cross-team reproducibility. Stay current with emerging models (GPT-4o, Claude 3.x, Gemini, Mistral) and benchmark their performance for internal use cases. What We Are Looking For Must Have Demonstrated experience designing production-grade prompts not just chatbot experiments. Hands-on knowledge of LangChain or LlamaIndex for building LLM-powered pipelines. Familiarity with embedding models and vector search (Pinecone, Weaviate, Chroma, or equivalent). Understanding of LLM internals: tokenization, context windows, temperature, top-p sampling. Experience running structured evaluations automated and human-in-the-loop. Strong written communication skills; prompts are documents too. Good to Have Experience with fine-tuning open-source models (LLaMA, Mistral) using LoRA / QLoRA. Working knowledge of Python for scripting evaluation pipelines and data wrangling. Exposure to compliance-sensitive AI applications (GDPR, HIPAA-adjacent use cases). Published prompt engineering work, open-source contributions, or a public GitHub portfolio.