About the Project
Implementing solutions in advanced technology streams and its sub systems with Gen AI technologies

Responsibilities

• Rapid Prompt Developing and Refinement - Develop and refine prompts to achieve specific coding goals, such as generating code snippets, fixing bugs, or writing unit tests.
• Context and Constraint Management - Understand how to provide Copilot with the necessary context (e.g., code base, language, framework) and constraints (e.g., specific libraries, coding style) to guide its suggestions.
• Prompt Experimentation and Testing - Conduct experiments to evaluate different prompt strategies and identify best practices.
• Prompt Documentation and Library Creation - Create and maintain a library of effective prompts or prompt chains for various tasks.
• User Education and Training - Develop tutorials and resources to help developers learn and improve their prompt engineering skills.
• Feedback and Iteration - Analyze Copilot’s responses and identify areas for improvement, suggesting updates to the model or prompting techniques.
• Team Collaborate – Collaborate with product teams, data scientists, and engineers to understand user needs and translate them into effective prompts.
• Prompt Experiments – Conduct experiments and research to test new prompting techniques and optimize existing workflows.
• Keeping ahead on Strategy – Stay up to date on the latest advancements in national language processing (NLP) and AI, and apply those insights to your work.
• Documenting to enable sharing of knowledge and Best Practices - Document and communicate your work clearly and concisely to technical and non-technical audiences for future reference

Skills/Requirement

• Hands-on experience with AI models and prompt engineering is invaluable.
• Proficiency in Python is highly recommended, as it’s widely used in AI development and NLP tasks.
• Exposure to Platforms like Coursera, edX, and others offer courses on AI, NLP, and prompt engineering.
• Familiarity with tools like TensorFlow or PyTorch can be beneficial.
• Analyzing AI outputs to identify what works and what needs improvement is crucial for prompt refinement.

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• Rapid Prompt Developing and Refinement - Develop and refine prompts to achieve specific coding goals, such as generating code snippets, fixing bugs, or writing unit tests.
• Context and Constraint Management - Understand how to provide Copilot with the necessary context (e.g., code base, language, framework) and constraints (e.g., specific libraries, coding style) to guide its suggestions.
• Prompt Experimentation and Testing - Conduct experiments to evaluate different prompt strategies and identify best practices.
• Prompt Documentation and Library Creation - Create and maintain a library of effective prompts or prompt chains for various tasks.
• User Education and Training - Develop tutorials and resources to help developers learn and improve their prompt engineering skills.
• Feedback and Iteration - Analyze Copilot’s responses and identify areas for improvement, suggesting updates to the model or prompting techniques.
• Team Collaborate – Collaborate with product teams, data scientists, and engineers to understand user needs and translate them into effective prompts.
• Prompt Experiments – Conduct experiments and research to test new prompting techniques and optimize existing workflows.
• Keeping ahead on Strategy – Stay up to date on the latest advancements in national language processing (NLP) and AI, and apply those insights to your work.
• Documenting to enable sharing of knowledge and Best Practices - Document and communicate your work clearly and concisely to technical and non-technical audiences for future referenc