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.