#💫 About Me

• 🔭 Currently Building: Reproducible AI workflows with LangChain and Gemini in Colab — modular pipelines, clean dependency management, and scalable design.
• 🧑‍🤝‍🧑 Open to Collaborate On:Open-source contributions (and starter issues) in TensorFlow & Matplotlib, focusing on explainability, visualization, and developer experience.
• 🤝 Seeking Help With: Optimizing document processing pipelines and enhancing vector store integration for large‑scale semantic search.
• 🌱 Actively Learning: Advanced debugging strategies, secure API key management, and deployment techniques using GCP and Streamlit.
• 💬 Ask Me About: Python, C++, reproducible Colab setups, backend architecture, and deploying AI tools with explainability.
• ⚡ Fun Fact: I turn workflow bottlenecks into automation hacks — and enjoy polishing GitHub READMEs for recruiter‑readiness.

Why: Built to create reproducible AI workflows in Colab with modular pipelines and clean dependency management.

Why: Designed to provide visualization and debugging utilities, making TensorFlow models more transparent and interpretable.

Why: Automates document ingestion and semantic search, turning static PDFs into searchable knowledge bases.

Why: Built to deploy AI models with explainability features, making them accessible to non-technical users.

Why: Provides pre-configured notebooks with pinned dependencies to ensure reproducibility and collaborative stability.