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Tejasri Vaspari

@tejasrivaspari

Message

I build enterprise RAG, NLP, and predictive machine learning solutions.

United States

Message

What I'm looking for

I'm looking to build practical machine learning, NLP, and Generative AI products, especially scalable RAG systems and predictive models that improve accuracy, retrieval quality, explainability, and real-world business outcomes.

At Databricks, I build enterprise Generative AI knowledge platforms that turn unstructured content into reliable answers. I develop RAG applications with Python, LangChain, LlamaIndex, LLMs, embeddings, and vector search.

I build document ingestion pipelines for PDFs, knowledge articles, and enterprise content, then improve retrieval through semantic search, hybrid retrieval, reranking, and metadata filtering. I also design prompts, grounding logic, and evaluation workflows to improve relevance and reduce hallucinations.

Previously at Tredence, I developed churn prediction, customer segmentation, and recommendation models using Python, Scikit-learn, XGBoost, and LightGBM. My work covered data preparation, feature engineering, model evaluation, explainability, and scalable experimentation with PySpark and MLflow.

I also build transformer-based NLP and document Q&A systems using PyTorch, Hugging Face, FAISS, and LangChain. I focus on practical AI applications with strong model accuracy, retrieval quality, explainability, and usability.

Experience

Work history, roles, and key accomplishments

Current

AI/ML Engineer

Current

Databricks

Jan 2025 - Present (1 year 8 months)

Developed enterprise RAG applications using Python, LangChain, LlamaIndex, LLMs, embeddings, and vector search. Built document ingestion pipelines, semantic search, and evaluation workflows to improve retrieval precision and response quality.

Python Lang Chain LlamaIndex LLM Embeddings Vector Search Hugging Face PyTorch MLFlow fastAPI RAG Semantic Search Prompt Engineering

Machine Learning Engineer

Tredence

Mar 2021 - Aug 2023 (2 years 5 months)

Developed machine learning models for customer churn prediction and segmentation using Python, Scikit-learn, XGBoost, and LightGBM. Performed feature engineering, model evaluation, and used PySpark and MLflow for scalable data preparation and experiment tracking.