Senior Data Scientist | GenAI & ML Systems | Team Lead
Designing AI systems that scale and teams that ship.

• Lead development of GenAI systems using LLMs, RAG, and agentic pipelines
• Architect ML solutions from experimentation to scalable production
• Drive data product strategy and cross-functional execution in e-commerce
• Mentor data teams and implement best practices for ML/AI delivery
• Translate complex business problems into real-world AI products

• LLM Agents for Clinical Trials
  Agentic LLM pipeline for automating trial eligibility checks and patient-trial matching.
  Integrates data analysis, compliance verification, and hallucination grading with human-in-the-loop workflows.
  Tech: LangGraph, OpenAI, Pydantic, Gradio

• Two-Stage RAG for Document QA
  Scalable RAG pipeline using two-stage retrieval — keyword + semantic search Boosts precision and cut compute cost. Achieved >75% reduction in retrieval overhead for enterprise-scale QA.
  Tech: LangChain, OpenAI, ChromaDB, Streamlit

• Data Science & ML Mini Tasks
  A curated set of focused, single-notebook projects that demonstrate applied ML and data science problem-solving.
  Topics include:

  • Ad Response Prediction
  • Predictive Modeling for Manufacturing Material Strength
  • Recipe Recommender, System Design
  • Customer Satisfaction Classifier
    Tech: Python, scikit-learn, XGBoost, Streamlit, pandas, matplotlib

• LLM Tutorials & Applications
  A collection of practical LLM architectures and end-to-end notebooks featuring carefully selected case studies across domains like healthcare, customer support, and product search.
  Includes RAG, tool-using agents, clinical trial retrieval, chatbot workflows, and document QA with real-world data sources.
  Tech: LangChain, OpenAI, RAG, ChromaDB, Pinecone, Streamlit

• Guardrails in LLM Apps – Strategies for implementing ethical safeguards, ensuring compliance, and enhancing security in Large Language Model applications.
• LLM Model Selection and Updates – Guidelines for selecting appropriate Large Language Models and managing their updates to balance quality, cost, and scalability in AI applications.
• Two-Stage RAG for Document QA – An innovative approach to document-based question answering using a two-stage retrieval strategy to enhance precision and scalability in Retrieval-Augmented Generation systems.
• Data Engineers: The Unsung Heroes Behind AI – An exploration of the pivotal role data engineers play in AI development, emphasizing their contributions to data quality, infrastructure, and the overall success of data science teams.

Feel free to reach out for collaboration, leadership opportunities, or just to swap ideas on building better GenAI systems.

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“Build AI that works — and teams that last.”