Job Description
We are seeking a visionary Senior Prompt Engineer (Direct Hire) to join our elite team in Richmond, VA. As a direct-hire opportunity, you will be at the forefront of integrating Generative AI into our core enterprise solutions. You will define the boundaries of what Large Language Models (LLMs) can achieve and translate complex data into seamless, human-like interactions.
In this pivotal role, you will bridge the gap between advanced AI models and practical business applications. You will work closely with Data Scientists, Backend Developers, and Product Managers to architect the interaction layer between humans and machines, ensuring our tools are not only powerful but also safe, accurate, and compliant.
Responsibilities
- Model Orchestration: Design, develop, and rigorously test complex prompts for LLMs (e.g., GPT-4, Claude, Llama) to drive specific business outcomes and automate workflows.
- RAG & Architecture: Collaborate with engineering teams to build Retrieval-Augmented Generation (RAG) pipelines and robust prompt management systems.
- Evaluation & Optimization: Continuously evaluate model outputs for accuracy, hallucination reduction, and tone consistency, implementing iterative improvements.
- Best Practices: Establish and document comprehensive prompt engineering standards and libraries to scale AI capabilities across the organization.
- Cross-Functional Leadership: Partner with stakeholders to identify high-impact use cases for AI and lead the technical execution of prompt strategies.
- Safety & Compliance: Implement guardrails and safety protocols to ensure AI outputs adhere to ethical guidelines and regulatory requirements.
Qualifications
- Experience: 3+ years of professional experience in Prompt Engineering, Natural Language Processing (NLP), or a related technical field.
- Technical Skills: Strong proficiency in Python and familiarity with API integrations (OpenAI, Anthropic, Hugging Face, LangChain).
- Communication: Exceptional writing and verbal communication skills with a deep understanding of language nuances, tone, and context.
- Technical Depth: Demonstrated experience with fine-tuning models or implementing RAG architectures.
- Education: Bachelor’s degree in Computer Science, Linguistics, or a related field (Master’s degree is a plus).
- Problem Solving: Strong analytical skills with the ability to debug complex AI behaviors and iterate rapidly on solutions.