Scope of Position:
We are looking for talented professionals to join us in advancing AI solutions. Our team is composed of passionate professionals in the AI field who thrive on solving complex problems and dedicated to developing cutting-edge technologies that transform industries and improve daily operations. The ideal candidate will have a strong background in LLM and Natural Language Processing (NLP), and will be providing technical leadership for designing, developing, and optimizing LLMs to enhance our AI-driven products and services.

Day to Day Responsibilities:

• Propose and lead research activities involving LLMs customization and optimization for different applications across Corning businesses.

• Design and implement LLMs using advanced RAG techniques such as Query Transformation, Index Retrieval, Re-Rank, etc. to ensure the application is precisely adapted and optimized for specific requirements.

• Develop LLM agents using LangGraph framework proficient in task planning and execution, ensuring smooth integration with external tools, databases, and APIs to tackle complex problems and improve decision-making.

• Develop and implement NLP models and algorithms to solve various language-related problems.

Minimum Qualifications:

• Proven hands-on experience in developing and deploying LLMs, especially the experience with Prompt Engineering and RAG to enhance the response capabilities of LLM.

• Familiar with LangChain architecture, connecting external tools, databases, and APIs.

• Deep understanding of NLP techniques, including transformers, attention mechanisms, and transfer learning.

• Proficiency in text data processing such as information retrieval, information extraction, text classification, feature engineering.

• Strong programming skills in Python and familiarity with machine learning frameworks such as TensorFlow, PyTorch, or Hugging Face.

• Master's degree in Computer Science, Engineering or similar field of study.

• Proficiency in English, both written and spoken, is required.

Preferred Qualifications:

• Publication record at top AI/ML venues.

• Familiarity with the training and fine-tuning processes of multimodal LLMs and Encoders.

• Familiarity with reinforcement learning and other advanced machine learning techniques.

• Contributions to open-source projects or research publications in relevant fields.

• Experience going beyond a research paper, taking ideas from experiments, to prototypes, to interactive demonstrations.

• Ph. D. in related field.