February 2025: Delivered across Vigtu/langflow and run-llama/llama_index with a focus on robust data handling, model-agnostic rerankers, and reliable notebook workflows. Key features delivered: Reworked rerankers to support NVIDIA and Cohere, plus LCCompressorComponent to compress documents into a DataFrame for faster, more maintainable data pipelines. Major bugs fixed: Hugging Face API endpoint now constructed correctly (endpoint fix) and finetune_embedding notebook import errors resolved by adding required dependencies (datasets, llama-index-embeddings-huggingface, transformers[torch]). Business impact: enhanced data throughput and reliability, reduced downtime, smoother experimentation and notebook runs. Technologies/skills demonstrated: Python, data processing with DataFrames, cross-model integration, API reliability, dependency management, and debugging across repositories.