class SnehVora:
role = "AI/ML Engineer @ Molina Healthcare USA"
education = "M.S. Computer Science, NJIT (GPA: 3.9)"
location = "Iselin, NJ 🌍"
experience = "4+ years"
focus = ["GenAI", "RAG Pipelines", "Fraud Detection", "LLM Evaluation"]
looking_for = "ML Engineer · AI Engineer · Backend Engineer"🏥 AI/ML Engineer – Gen AI · Molina Healthcare USA · Jan 2025 – Present
- Built a RAG-based GenAI assistant using LangChain + Azure OpenAI + Azure AI Search → reduced manual lookup time 30%
- Ingested and chunked 8,000+ healthcare policy pages via PyMuPDF pipelines into Azure Blob Storage
- FAISS embedding retrieval + metadata filters improved document retrieval accuracy 22%
- Applied PHI masking, RBAC controls, and guardrails for HIPAA-aligned GenAI usage
🏦 Machine Learning Engineer · Accenture India · Jun 2021 – Nov 2023
- Fraud detection system analyzing 1M+ BFSI transactions — XGBoost achieving ~0.86 ROC-AUC
- Engineered 40+ fraud-risk features; Isolation Forest anomaly detection; SMOTE improved recall ~20%
- Batch scoring automation reduced manual analysis effort ~30%
GenAI & LLM
ML / DL
Backend & Cloud
| Project | Stack | Highlight |
|---|---|---|
| Legal AI Assistant | LangChain · FastAPI · Pinecone · RAG | 90% faster research · 50K+ daily requests |
| Smart Inventory Bot | LangGraph · SQLite · Redis · Streamlit | Natural language → SQL · 100% query complexity reduction |
| Multi-Agent RL | PyTorch · OpenAI Gym | Continuous control with pixel inputs |
| WPInsight Automator | Python · Selenium · ETL · DigitalOcean | End-to-end content pipeline automation |
| Waste ML Classifier | PyTorch · VGG19 · InceptionV3 | CNN transfer learning for waste classification |
🎓 M.S. Computer Science — New Jersey Institute of Technology (NJIT) · GPA: 3.9 · Dec 2025
🎓 B.Tech Computer Science — Charotar University of Science and Technology · GPA: 3.36
📜 IBM Certified Data Architect – Big Data | ML Course – Wissenaire, IIT Bhubaneswar
Twitter Sentiment Analysis with TextBlob — IJISRT, Vol. 7, Issue 11, Nov 2022
Designed a pipeline for Twitter data extraction, cleaning, and sentiment classification using Python's TextBlob.