Prompt Engineer - (Princeton, NJ)

Position: Prompt Engineer
Location: Princeton, NJ

Job Description:
We are seeking a Prompt Engineer with expertise in creating, refining, and optimizing natural language prompts for large language models (LLMs).

Key Responsibilities:

• Prompt Development and Optimization Design and develop effective prompts to guide LLM behavior and responses for specific use cases.
• Experiment with various prompt formats and structures to improve model performance.
  Analyze and optimize prompts for accuracy, consistency, and relevance across diverse scenarios.
• Model Tuning and Customization
• Collaborate with machine learning engineers to fine-tune LLMs using tailored datasets and prompts.
• Develop prompt templates for domain-specific applications, ensuring they align with business objectives.
• Validate and evaluate model outputs to refine prompt effectiveness.
  Collaboration and Use Case Development.
• Work with cross-functional teams to understand requirements for specific AI-powered features or applications.
• Translate business needs into actionable tasks by designing context-aware prompts.
• Provide guidance on integrating prompt engineering into broader AI development pipelines.
  Experimentation and Evaluation:
• Conduct A/B testing and experiments to measure the impact of different prompt strategies.
  Use tools like
• OpenAI API, LangChain, or similar frameworks to implement and test prompts.
• Collect feedback on model responses to iteratively improve prompt quality.
• Documentation and Knowledge Sharing
• Document prompt engineering techniques, experiments, and best practices.
• Share findings and insights with stakeholders to inform AI strategies.
• Provide training or workshops on prompt engineering for internal teams.

Key Qualifications:

• Technical Expertise
• Strong understanding of large language models (LLMs) and their applications (e.g., GPT, Bard, Claude).
• Hands-on experience with APIs and frameworks like OpenAI, LangChain, or similar.
• Familiarity with programming languages such as Python for LLM integration and testing.
• Proficiency in natural language processing (NLP) concepts and techniques.
• Problem Solving and Creativity.
• Ability to design creative and effective prompts to solve complex problems.
• Skilled in identifying edge cases and improving model responses.
• Analytical mindset for evaluating and iterating on prompt performance.