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Tejasvaidya10/README.md

Tejas Vaidya

AI/ML Engineer · M.S. Data Science & Analytics @ Georgia State University

I build production-grade LLM and machine learning systems. Currently a Graduate Research Assistant at Truist Bank, where I developed a RAG evaluation framework for an enterprise AI chatbot using AWS Bedrock and LLM-as-Judge methodology.

Open to Data Scienece & AI/ML Engineer Full Time Roles — based in United States.


What I'm working on

  • Agentic RAG evaluation framework — production pipeline for measuring retrieval quality, answer faithfulness, and groundedness on an enterprise chatbot. Python, AWS Bedrock, LLM-as-Judge.
  • LapLens — full-stack F1 telemetry analytics platform. React + FastAPI, FastF1 data pipeline, deployed on Oracle Cloud + Cloudflare Pages.
  • ARIA — 4-stage GenAI pipeline for insurance underwriting. PDF ingestion with custom spaCy NER, XGBoost + SHAP risk classification, FAISS retrieval, and Claude tool-use orchestration. Live demo ↗
  • VoxVerity — research study on why audio deepfake detectors that ace benchmarks collapse on real-world audio, with a pre-registered fix and an honestly-reported negative result. PyTorch, wav2vec2, LLM-based explainability.
  • Coursework in Deep Learning, Machine Learning, and GenAI Solutions (Spring 2026).

Selected projects

Project What it does Stack
LapLens F1 telemetry analytics — lap comparisons, sector analysis, driver performance over a race weekend. React, FastAPI, FastF1, Oracle Cloud, Cloudflare Pages
ARIA · live demo ↗ GenAI underwriting assistant — PDF → custom NER → XGBoost + SHAP → FAISS retrieval → Claude tool-use synthesis. 4 microservices, 81 tests. FastAPI, spaCy, XGBoost, SHAP, FAISS, Claude, React, Docker
VoxVerity Audio deepfake detection research — measures the benchmark-to-real-world generalization gap, diagnoses the failure mode via LLM-based explainability, and tests a pre-registered fix. PyTorch, wav2vec2, FastAPI, Claude, SHAP-style rationale scoring
MedSift AI (Hacklytics 2026) Privacy-first healthcare conversation intelligence — audio in, SOAP notes + risk score + trial matches out. Fully local: Whisper + Presidio PHI redaction + Ollama (LLaMA 3.1), zero cloud APIs. FastAPI, Whisper, Presidio, Ollama, Streamlit, SQLite FTS5
SparkPath (🥉 AI for Good hackathon) AI-powered youth career discovery platform — personalized career pathways, mentorship matching, wellness tracking. React, Node.js, AWS Bedrock, DynamoDB, Socket.io

Pinned repos below have the full write-ups.


Tech I work with

LLMs & GenAI — RAG pipelines, AWS Bedrock, LLM-as-Judge evaluation, prompt engineering, embeddings ML & Deep Learning — PyTorch, TensorFlow, scikit-learn, XGBoost, ensemble methods, SVMs, GAMs Data & Backend — Python (pandas, NumPy), SQL (MySQL, PostgreSQL), FastAPI, R Infra & MLOps — Docker, AWS, GitHub Actions (CI/CD), Oracle Cloud Viz — Tableau, Power BI, matplotlib, Plotly


Connect


"Somewhere, something incredible is waiting to be known." — Carl Sagan

Pinned Loading

  1. ARIA ARIA Public

    Explainable GenAI pipeline for insurance underwriting — PDF → NER → XGBoost + SHAP → FAISS RAG → Claude tool-use synthesis

    Python

  2. laplens laplens Public

    F1 telemetry analytics dashboard — compare drivers head-to-head across lap times, sector analysis, and tire strategy with real race data

    TypeScript

  3. MedSift-Ai MedSift-Ai Public

    Privacy-first healthcare conversation intelligence — audio in, SOAP notes + risk score + clinical trial matches out. Fully local: Whisper + Presidio + Ollama, zero cloud APIs

    TypeScript

  4. spark spark Public

    AI-powered youth career discovery platform — personalized career pathways, mentorship matching, and wellness tracking. Won 3rd prize at an AI for Good hackathon

    TypeScript

  5. VoxVerity VoxVerity Public

    Why audio deepfake detectors that ace benchmarks collapse on real-world audio, and a pre-registered attempt to fix it — ASVspoof5 to In-the-Wild, with LLM-based failure explainability.

    Python