Prompt Engineer- Full Time Role- 8+years

Role : Prompt EngineerLocation : 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 OverviewPrimary 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.