Ai Prompt Engineer

Moonfire; Texas; Hybrid; USD 120,000 - 170,000; Full time

Job summary

Moonfire is seeking an AI Prompt Engineer in Irving, TX for a hybrid, full-time role. You will design and optimize prompts, manage context windows, and implement GenAI workflows across OpenAI, Gemini, and Claude APIs.

Proficiency with LangChain/LlamaIndex, knowledge graphs, and tool integration is essential to deliver scalable, compliant GenAI solutions. The role emphasizes clear technical communication, Python scripting, and collaboration with data teams to refine prompts and ensure robust

Qualifications

• Understanding of tokenization, context windows, and APIs (such as OpenAI, Anthropic, or Azure OpenAI). Python for GenAI, data preprocessing, and scripting.
• Foundation models, LLMs, tokenization, embeddings, and context windows.
• Hands-on prompt/context engineering and practical RAG experience.
• Knowledge graphs and Graph RAG concepts.
• Exposure to agentic AI, tool-using agents, multi-step workflows, and frameworks such as ADK, LangGraph, CrewAI, or the OpenAI Agents SDK.
• Awareness of tool/function calling, MCP, A2A, agent harness, memory, and guardrails.
• Experience with GenAI APIs such as OpenAI, Gemini, and Claude; orchestration frameworks such as LangChain or LlamaIndex; Docker, Git, and AI compliance/privacy principles.
• Communication & Linguistics: clear writing and phrasing.
• Technical Skills: Python/SQL or software workflow knowledge.

Skills

LLM Familiarity

GenAI Concepts

Prompt Engineering

Knowledge Graphs

Agentic AI

APIs & Orchestration

Tool Use & Guardrails

GenAI APIs

Communication Skills

Python Basics

Tools

LangChain

LlamaIndex

LangGraph

OpenAI SDK

Docker

Git

Job description

Role: Ai Prompt Engineer

Location: Irving, TX

Job Type: Hybrid

Job Role: Full Time

Common Qualifications & Skills

• AI & LLM Familiarity: Understanding of tokenization, context windows, and APIs (such as OpenAI, Anthropic, or Azure OpenAI). Python for GenAI, data preprocessing, and scripting.
• Core GenAI concepts: foundation models, LLMs, tokenization, embeddings, and context windows.
• Hands-on prompt/context engineering and practical RAG experience.
• Working knowledge of knowledge graphs and Graph RAG.
• Exposure to agentic AI, tool-using agents, multi-step workflows, and frameworks such as ADK, LangGraph, CrewAI, or the OpenAI Agents SDK.
• Awareness of tool/function calling, MCP, A2A, agent harness, memory, and guardrails.
• Experience with GenAI APIs such as OpenAI, Gemini, and Claude; orchestration frameworks such as LangChain or LlamaIndex; Docker, Git, and AI compliance/privacy principles.
• Communication & Linguistics: Strong command of syntax, writing, and clear logical phrasing.
• Technical Skills: Varying by role—some positions focus purely on writing and psychology, while technical roles require basic Python, SQL, or software workflow knowledge.