NLP Data Scientist

We are seeking a talented and motivated Data Scientist specializing in Large Language Models (LLMs) to join our team at [Company Name]. You will play a pivotal role in designing, fine-tuning, and implementing advanced conversational AI models to enhance our customer service solutions. This position focuses on tailoring LLMs to effectively handle a wide range of customer interactions, including bookings, payments, and exception scenarios such as managing angry customers, responding to sarcastic remarks, addressing out-of-context questions, and ensuring responses are contextually accurate and empathetic.

Key Responsibilities

1. Model Fine-Tuning and Optimization
   
• Fine-tune large language models (e.g., OpenAI, GPT, or other state-of-the-art frameworks) to align with business-specific customer service needs.
  
• Adjust model parameters to improve performance on use-case-specific datasets.
  
2. Development and Training
   
• Design and implement training workflows using retrieval-based techniques, prompt engineering, and fine-tuning as necessary.
  
• Build and maintain datasets, including creating, annotating, and cleaning customer interaction data for training purposes.
  
3. Response Contextualization
   
• Develop models that can handle complex customer queries with contextual relevance, ensuring responses are coherent, empathetic, and aligned with business objectives.
  
• Incorporate strategies to handle sarcasm, emotional outbursts, and out-of-context queries gracefully.
  
4. Integration and Deployment
   
• Collaborate with engineering teams to deploy and monitor the performance of LLMs in production systems.
  
• Optimize the models for scalability, latency, and cost-effectiveness in a high-traffic environment.
  
5. Research and Innovation
   
• Stay updated on advancements in LLMs, conversational AI, and customer service automation.
  
• Experiment with state-of-the-art techniques like reinforcement learning with human feedback (RLHF) to improve model performance.
  
6. Evaluation and Feedback Loop
   
• Define and measure key performance indicators (KPIs) such as accuracy, relevance, and customer satisfaction.
  
• Incorporate feedback loops to iteratively refine models based on user interactions and edge cases.
  

Requirements

• Experience:
  
• Proven experience working with large language models (e.g., OpenAI GPT, LLaMA, etc.) and fine-tuning techniques.
  
• Hands-on experience with conversational AI systems in customer service or similar domains.
  
• Technical Expertise:
  
• Strong programming skills in Python, with proficiency in machine learning libraries such as TensorFlow, PyTorch, or Hugging Face Transformers.
  
• Expertise in prompt engineering, retrieval-based techniques, and fine-tuning pipelines.
  
• Familiarity with vector databases and retrieval-augmented generation (RAG) frameworks.
  
• Experience with cloud platforms (e.g., GCP, AWS, or Azure) for AI model deployment.
  
• Soft Skills:
  
• Exceptional problem-solving skills with attention to detail.
  
• Strong communication skills to collaborate across teams and explain technical concepts to non-technical stakeholders.
  
• Empathy and customer-centric mindset to design effective conversational solutions.
  

Nice to Have

• Experience in reinforcement learning with human feedback (RLHF).
  
• Familiarity with ethical AI practices and handling sensitive customer data.
  
• Knowledge of sentiment analysis, emotion detection, and behavioral modeling in AI systems.
  

Benefits