Tejas Nadgauda

I’m a 3rd-year B.E. Data Science student with hands-on experience building GenAI systems, RAG pipelines, LLM automation, and MLOps batch jobs. I enjoy turning messy inputs into useful outputs—whether that’s semantic retrieval, resume scoring, or automated pipelines that run reliably.

I’m proficient in Python and modern AI tooling, including TensorFlow, LangChain, FAISS, and Gemini API, and I use Docker to keep deployments reproducible. I also work with practical data skills like Pandas and NumPy, and I’m comfortable building end-to-end workflows—from prompts and LLM integration to structured logging and measurable results.

In my RAG-based PDF Question Answering System, I built a multi-PDF Q&A app using chunking and FAISS indexing with Gemini embeddings, and deployed a Streamlit UI supporting 10+ documents. In an MLOps Batch Pipeline (Trading Signal Engine), I processed 10,000-row OHLCV data with deterministic seed control via YAML config, Dockerized the pipeline, and produced machine-readable metrics.json using structured logging. I also automated a job application workflow with n8n and Gemini API, reducing manual job application prep time by ~70%.

I’m actively seeking AI/ML or Software Engineering internship roles where I can contribute by shipping AI-enabled products and dependable pipelines. I bring a builder’s mindset—focused on prompt engineering, retrieval quality, operational stability, and clear, testable outcomes.