We are seeking a skilled Lead Software Engineer to join our team and lead a project focused on developing GenAI applications using Large Language Models (LLMs) and Python programming.

In this role, you will be responsible for designing and optimizing Al-generated text prompts to maximize effectiveness for various applications. You will also collaborate with cross-functional teams to ensure seamless integration of optimized prompts into the overall product or system. Your expertise in prompt engineering principles and techniques will allow you to guide models to desired outcomes and evaluate prompt performance to identify areas for optimization and iteration.

Responsibilities :

- Design, develop, test and refine AI-generated text prompts to maximize effectiveness for various applications

- Ensure seamless integration of optimized prompts into the overall product or system

- Rigorously evaluate prompt performance using metrics and user feedback

- Collaborate with cross-functional teams to understand requirements and ensure prompts align with business goals and user needs

- Document prompt engineering processes and outcomes, educate teams on prompt best practices and keep updated on the latest AI advancements to bring innovative solutions to the project

Requirements :

- 7 to 12 years of relevant professional experience

- Expertise in Python programming including experience with Al/machine learning frameworks like TensorFlow, PyTorch, Keras, Langchain, MLflow, Promtflow

- 2-5 years of working knowledge of NLP and LLMs like BERT, GPT-3/4, T5, etc. Knowledge of how these models work and how to fine-tune them

- Expertise in prompt engineering principles and techniques like chain of thought, in-context learning, tree of thought, etc.

- Knowledge of retrieval augmented generation (RAG)

- Strong analytical and problem-solving skills with the ability to think critically and troubleshoot issues

- Excellent communication skills, both verbal and written in English at a B2+ level for collaborating across teams, explaining technical concepts, and documenting work outcomes