Responsibilities:

• Responsible for the research and implementation of related algorithms in the fields of customer service scheduling optimization.

• In-depth understanding of customer service scheduling systems with the business domain knowledge to take the impact of AI product to the next level

• Develop and fine-tune Large Language Models (LLMs) to derive actionable insights and enhance business decision-making processes.

• Work as prompt engineers to refine and optimize prompt design, enabling more accurate and contextually relevant outputs from LLMs.

• Develop scalable and robust LLM/RAG frameworks that can be adapted to customer service scheduling, driving innovation and maintaining a competitive edge in the market.
  
  

Qualifications:

• Master's degree or higher in Computer Science, Data Science, Statistics, Mathematics, Computational Linguistics, or a related field.

• A minimum of 3 years of relevant industry experience in AI/ML. Hands-on experience in the field of machine learning, especially in large language models (LLMs) and generative artificial intelligence.

• Proficiency in programming languages such as Python, Java, with experience in machine learning (ML), natural language processing (NLP) libraries, and deep learning frameworks such as TensorFlow, PyTorch, Scikit-learn, SpaCy, and NLTK.

• Experience in the customer service field is required. Knowledge of the customer service scheduling process is necessary, and experience with related projects is preferred.