Prompt Engineer- Full Time Role- 8+years

Moonfire; Irving (TX); Hybrid; USD 110,000 - 140,000; Full time

Job summary

Moonfire in Irving, TX is seeking a Prompt Engineer to design, test, and optimize prompts for large language models, ensuring outputs are accurate, safe, and useful. You will craft system instructions, develop context guidelines, and support RAG workflows, while collaborating with data scientists and software engineers on reliable AI solutions.

This hybrid role requires strong communication, knowledge of tokenization, embeddings, APIs such as OpenAI, and experience with frameworks like LangChain

Qualifications

• Understanding of tokenization, context windows, and APIs (OpenAI, Anthropic, or Azure OpenAI).
• GenAI concepts: foundation models, LLMs, embeddings, tokenization.
• Hands-on prompt/context engineering and RAG experience.
• Knowledge graphs and Graph RAG experience.
• Exposure to agentic AI, tool-using agents, and frameworks such as ADK, LangGraph, CrewAI, or OpenAI Agents SDK.
• Awareness of tool/function calling, MCP, A2A, memory and guardrails.
• Experience with GenAI APIs (OpenAI, Gemini, Claude) and orchestration frameworks (LangChain, LlamaIndex).
• Familiarity with Docker, Git and AI compliance/privacy.

Responsibilities

• Design, test, and optimize prompts for chatbots, copilots, and RAG systems.
• Support context engineering, prompt engineering, and RAG workflows with embeddings and semantic search.
• Assist with deployment, monitoring, data preprocessing, API development, and code reviews.
• Test model outputs for accuracy, safety, latency, and token cost.

Skills

AI & LLM familiarity

Python for GenAI

Context windows

Tokenization

RAG experience

Knowledge graphs

MCP exposure

LangChain

LangGraph

Tools

LangGraph

CrewAI

OpenAI API

LlamaIndex

Docker

Git

Job description

Role : Prompt Engineer

Location : Irving, TX (Hybrid)

Job Role : Full Time

Job Description-

A prompt engineer designs, tests, and optimizes instructions (prompts) for large language models (LLMs) to ensure the AI generates accurate, safe, and useful outputs.

Role Overview

Primary Goal:

Bridge the gap between human business needs and artificial intelligence capabilities.

Core Function:

Craft system instructions, few-shot examples, and context guidelines, so AI tools produce reliable responses instead of hallucinations (false or made-up information).

Key Responsibilities

• Design & Optimization: Write and refine prompts for chatbots, virtual copilots, and retrieval-augmented generation (RAG) systems. Support context engineering, prompt engineering, and RAG workflows, including chunking, embeddings, semantic search, and knowledge graphs.
• Help build agentic workflows and AI agents with frameworks such as Google ADK, LangGraph, or CrewAI.
• Connect agents with external tools/data via MCP; A2A exposure is a plus.
• Assist with deployment, monitoring, data preprocessing, API development, and code reviews
• Testing & Evaluation: Test model outputs against accuracy, safety, latency (response delay), and token cost metrics.
• Library Management: Build and maintain reusable prompt templates and governance standards for enterprise teams.
• Adversarial Testing: Run stress tests to check for prompt injections (tricking the AI into breaking rules), data leaks, and bias.
• Cross-Functional Collaboration: Partner with data scientists, software developers, product managers, and business stakeholders.

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.