Data Scientist - I (Prompt Engineer)

Job Responsibilities:

• Prompt Engineering and Development: Design, craft, and optimize high-quality prompts to elicit desired responses from AI models. Ensure prompts are clear, coherent, and relevant to business needs, improving AI model performance and user satisfaction.


• Model Design and Implementation: Develop and implement machine learning algorithms for NLP/LLMs and deep learning models at scale to meet business requirements. Analyze large datasets to create models that predict, generate responses, summarize, and identify patterns.


• Benchmarking and Evaluation: Conduct benchmarking studies to compare prompt performance and establish best practices. Develop evaluation frameworks to assess prompt effectiveness using qualitative and quantitative metrics.


• Collaboration and Communication: Work closely with cross-functional teams, including data scientists and ML engineers, to identify business problems and develop AI solutions. Communicate findings, insights, and recommendations to stakeholders in a clear and concise manner.


• Documentation and Research: Maintain comprehensive documentation of prompt development, optimization techniques, and evaluation results. Stay up-to-date with the latest NLP and AI research, incorporating cutting-edge techniques into prompt engineering and model development.


• Continuous Improvement and Learning: Regularly refine and optimize prompts and models to enhance performance, reduce ambiguity, and improve user interaction. Drive continuous learning and skill development by staying informed about advancements in NLP, machine learning, and AI technologies.

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Applied Mathematics, Statistics, Linguistics, or a related field.
• 2+ years of experience in Data Science, focusing on NLP, prompt engineering, or deep learning.
• Proficiency in Python and familiarity with AI frameworks and libraries (e.g., TensorFlow, PyTorch, Hugging Face).
• Strong understanding of NLP techniques and machine learning principles, including sentiment analysis, word embedding, text classification, and Transformer models (e.g., GPT, BERT).
• Experience with developing, optimizing, and evaluating prompts to improve AI model responses.
• Experience with large language models (e.g., GPT-4, Llama3) and their applications in customer service or other domains.
• Knowledge of data visualization tools and techniques to communicate complex data insights effectively.
• Background in linguistics or computational linguistics.
• Experience with agile development methodologies and big data platforms like Hadoop or Spark.
• Excellent analytical, problem-solving, and communication skills.
• Ability to work collaboratively in a team environment, manage multiple projects simultaneously, and document complex processes effectively.
• Self-motivated with a passion for continuous learning and innovation in AI.