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.