- 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.

Qualifications and Skills:

- Education: Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field (PhD is a plus).

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.