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# AI Prompt Engineer

- **Company:** Peoplentech Llc
- **Location:** Alexandria, VA, United States
- **Experience:** Experienced
- **Salary:** $120,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Artificial Intelligence, Python (Programming Language), Knowledge Management, OpenAI, Pinecone, Enterprise Software Applications, LangChain, ReactJS, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Llamaindex, Generative AI, AI Platforms, Claude, Virtual Agents, Semantic Kernel, GPT
- **Published:** October 29, 2026
- **Apply:** https://www.thejobnetwork.com/job/579724aa-685f-4d66-baa8-3da465b826fd/ai-prompt-engineer

## About the Role

* Strong understanding of Generative AI, LLMs, and Prompt Engineering concepts.
 * Hands-on experience with OpenAI/ Anthropic Claude or similar AI platforms.
 * Knowledge of prompt patterns such as:
 + Zero-shot
 + Few-shot
 + Chain-of-Thought (CoT)
 + Tree-of-Thought (ToT)
 + ReAct
 + Role-based prompting
 * Experience with Python and AI development frameworks.
 * Familiarity with any of the frameworks like LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent frameworks.
 * Understanding of RAG architectures and vector databases such as Pinecone etc
 * Strong analytical and problem-solving skills.
 * Excellent communication and documentation abilities.

## Description

We are seeking an innovative and detail-oriented AI Prompt Engineer to design, optimize, and evaluate prompts for Generative AI and Large Language Model (LLM) applications. The ideal candidate will work closely with business stakeholders, data scientists, AI engineers, and product teams to develop high-quality prompts that improve AI accuracy, reliability, and user experience across enterprise use cases., * Design, develop, test, and refine prompts for LLMs such as GPT /Claude .

 * Create prompt engineering best practices for enterprise applications.
 * Evaluate model outputs and continuously optimize prompts for accuracy, relevance, safety, and performance.
 * Develop prompt libraries, prompt chains, and AI agent workflows.
 * Work with RAG (Retrieval Augmented Generation), vector databases, and knowledge management systems to improve contextual responses.
 * Monitor AI application performance and implement prompt improvements based on user feedback.
 * Document prompt engineering methodologies, testing results, and governance standards.