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.