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.
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