Prompt Engineer

NetomiToronto, ON

About The Position

We are looking for a Prompt Engineer to craft, optimize, evaluate, and benchmark prompts for enhanced AI performance at Netomi. This role requires expertise in NLP, AI behavior, and customer-specific business rules. You will collaborate with the Customer Success team and data scientists to develop customized, effective AI solutions.

Requirements

  • Bachelor or Master s degree in Computer Science, Data Science, Linguistics, or a related field.
  • Strong understanding of natural language processing (NLP) and machine learning principles.
  • Experience with AI prompt engineering, including writing, optimizing, and evaluating prompts.
  • Proficiency in programming languages such as Python.
  • Familiarity with AI frameworks and libraries (e.g., TensorFlow, PyTorch, Hugging Face).
  • Excellent analytical and problem-solving skills.
  • Strong written and verbal communication skills.
  • Ability to work collaboratively in a team environment and manage multiple projects simultaneously.

Nice To Haves

  • Should have a minimum of 5 years of working in data science
  • Experience with large language models (e.g., GPT-3, GPT-4, Llama3) and their applications.
  • Knowledge of data visualization tools and techniques.
  • Background in linguistics or computational linguistics.
  • Experience with agile development methodologies.

Responsibilities

  • Prompt Development: Design and refine client-specific prompts, ensuring accuracy and relevance. Define tool descriptions for agentic frameworks to enhance AI interactions.
  • Optimization & Testing: Improve prompts for clarity and performance, automate testing with scripts, and evaluate LLMs for best-fit solutions.
  • Evaluation & Benchmarking: Develop evaluation frameworks and benchmark prompts to establish best practices.
  • Collaboration & Documentation: Work with Customer Success and Data Science teams, maintaining clear documentation on prompt development and optimization.
  • Research & Innovation: Stay updated on NLP advancements, experiment with new prompting strategies, and refine model-specific adaptations.
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