Data Scientist - Generative AI

Key / Primary Responsibilities: - Lead cross-functional teams in the design, development, and deployment of Generative AI solutions, with a strong focus on Large Language Models (LLMs). - Architect, train, and fine-tune state-of-the-art LLMs (e.g., GPT, BERT, T5) for various business applications, ensuring alignment with project goals. - Deploy and scale LLM-based solutions, integrating them seamlessly into production environments and optimizing for performance and efficiency. - Develop and maintain machine learning workflows and pipelines for training, evaluating, and deploying Generative AI models, using Python or R, and leveraging libraries like Hugging Face Transformers, TensorFlow, and PyTorch. - Collaborate with product, data, and engineering teams to define and refine use cases for LLM applications such as conversational agents, content generation, and semantic search. - Design and implement fine-tuning strategies to adapt pre-trained models to domain-specific tasks, ensuring high relevance and accuracy. - Evaluate and optimize LLM performance, including handling challenges such as prompt engineering, inference time, and model bias. - Manage and process large, unstructured datasets using SQL and NoSQL databases, ensuring smooth integration with AI models. - Build and deploy AI-driven APIs and services, providing scalable access to LLM-based solutions. - Use data visualization tools (e.g., Matplotlib, Seaborn, Tableau) to communicate AI model performance, insights, and results to non-technical stakeholders. Key / Primary Responsibilities: - Lead cross-functional teams in the design, development, and deployment of Generative AI solutions, with a strong focus on Large Language Models (LLMs). - Architect, train, and fine-tune state-of-the-art LLMs (e.g., GPT, BERT, T5) for various business applications, ensuring alignment with project goals. - Deploy and scale LLM-based solutions, integrating them seamlessly into production environments and optimizing for performance and efficiency. - Develop and maintain machine learning workflows and pipelines for training, evaluating, and deploying Generative AI models, using Python or R, and leveraging libraries like Hugging Face Transformers, TensorFlow, and PyTorch. - Collaborate with product, data, and engineering teams to define and refine use cases for LLM applications such as conversational agents, content generation, and semantic search. - Design and implement fine-tuning strategies to adapt pre-trained models to domain-specific tasks, ensuring high relevance and accuracy. - Evaluate and optimize LLM performance, including handling challenges such as prompt engineering, inference time, and model bias. - Manage and process large, unstructured datasets using SQL and NoSQL databases, ensuring smooth integration with AI models. - Build and deploy AI-driven APIs and services, providing scalable access to LLM-based solutions. - Use data visualization tools (e.g., Matplotlib, Seaborn, Tableau) to communicate AI model performance, insights, and results to non-technical stakeholders.