Fine-tuning large language models often highlights inefficiencies in AI workflows, particularly the disjointed nature of data engineering and model training, which traditionally require separate processes and systems, leading to data silos and increased latency. Bodo addresses this issue by offering a unified, high-performance pipeline that integrates data loading, preprocessing, and training within a single application, using familiar Python APIs. By utilizing Bodo DataFrames and Bodo AI Toolkit, users can load data directly from sources like Apache Iceberg and seamlessly transition it into distributed PyTorch training jobs without the need for intermediate file storage, thus maintaining strong schemas and version control. This approach eliminates the traditional separation between data engineering and machine learning, enhancing scalability and efficiency by leveraging MPI-based high-performance computing technology and an auto-parallelizing JIT compiler to accelerate Python workloads from laptops to clusters. The integration of Bodo's technology enables a streamlined workflow from raw data to a fine-tuned model, as demonstrated by the example of training a Llama 3.1 8B model using LoRa for chatbot enhancement, showcasing the potential for HPC-grade performance across the entire AI pipeline.