Prompt Engineering

Key Skills: Gen AI, NLP, Python

Roles and Responsibilities:

• Develop, test, and optimize prompts for large language models to meet business use cases.
• Collaborate with data scientists, ML engineers, and business stakeholders to design tailored AI solutions.
• Translate business requirements into NLP tasks, ensuring outputs are accurate, relevant, and ethical.
• Apply advanced NLP techniques for data extraction, classification, summarization, and generation.
• Create reusable Python packages and APIs for prompt-related utilities and applications.
• Evaluate and refine different prompting strategies (e.g., zero-shot, few-shot, chain-of-thought) based on the use case.
• Conduct rigorous testing and validation of prompts to ensure model robustness and expected behavior.
• Maintain clean, modular, and well-documented code following best practices in software engineering.
• Contribute to the development of knowledge bases and libraries for prompt templates and use cases.

Skills Required:

• Minimum 5 years of technical experience, preferably in financial services or a highly regulated industry.
• Strong Python programming experience and data manipulation using relevant Python libraries (e.g., pandas, numpy, re).
• Experience creating custom Python packages, RESTful APIs, and writing comprehensive unit tests.
• Solid understanding of software engineering principles, including object-oriented programming (OOP), Git/GitHub workflows, and clean code practices.
• Hands-on experience with NLP frameworks and libraries (e.g., spaCy, NLTK, HuggingFace Transformers).
• Proficiency in handling large-scale datasets and data preprocessing for NLP tasks.
• Deep understanding of prompting strategies for LLMs and when to apply them effectively.
• Experience working closely with non-technical business stakeholders to understand and meet functional goals.

Preferred Qualifications:

• Experience working with LLMs (OpenAI, Anthropic, Cohere, etc.) and fine-tuning/customizing models.
• Knowledge of ethical AI practices and governance around generative AI.
• Exposure to cloud platforms (AWS, GCP, Azure) for deploying AI solutions.
• Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG) systems.

Education:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Computational Linguistics, or a related field