Chetan Valluru
Data Scientist • LLM Engineer • ML Specialist
Building production LLM agents, RAG systems, and on-device ML solutions. Specialized in generative AI, NLP, and MLOps — reducing costs 60%, accelerating insights 80%, achieving 99.9% accuracy. Expert in PyTorch, Transformers, fine-tuning (LoRA), quantization, and privacy-preserving deployment.
Chetan Valluru
Focused on building production-grade LLM agents, scalable RAG systems, and advanced generative AI frameworks.
View ProfileAbout Me & Education
lightbulb My Mission
I build production AI systems that solve real problems. From architecting NL-to-SQL platforms with LLM-powered NLP to deploying privacy-preserving on-device ML, I focus on shipping reliable, optimized solutions. My work has reduced data tickets 30%, accelerated insights 80%, and enabled self-serve analytics for cross-functional teams. I specialize in LLMs, RAG, fine-tuning, quantization, and MLOps — always balancing performance, cost, and security.
school Education
Rutgers University — M.S. Data Science
Focus: LLMs, Generative AI, MLOps, System Design
VNR VJIET — B.Tech Electronics & Communication
Specialization: AI & Machine Learning
Distinctions
IEEE Research Publication
Multi-Classification of Respiratory Diseases using Deep Learning
2023 ICSCSS • DOI: 10.1109/ICSCSS57650.2023.10169597
HackRU Fall 2024 — 1st Place
NJ Transit Track Winner
ML delay prediction (82% accuracy) + RAG chatbot serving 15K+ queries.
Global Youth Scientist
GOLD Award
Recognized for applied AI research and community impact in international STEM competition.
Experience
Data Scientist
JerseySTEM, NJ
- Architected production NL-to-SQL platform using LLM-powered NLP, LangChain, and schema-aware RAG; reduced data tickets 30%, accelerated insights 80%, enabled self-serve analytics for 8+ teams.
- Engineered MLOps pipeline for LLM deployment with NLP evaluation harness (faithfulness, semantic similarity, precision/recall) and automated testing; achieved 95% grounded responses via optimized LLM inference.
- Implemented LLM security: NLP-based PII detection, RBAC, audit logging, prompt injection mitigation; reduced LLM hallucinations 40%, maintained 99.8% uptime Q4 2025.
Data Science Virtual Intern
British Airways via Forage
Conducted NLP sentiment analysis on 3,949+ reviews; built predictive models to map buying patterns and proposed experiments for ~20% campaign ROI lift.
Machine Learning & Statistics Grader
Rutgers University
Reviewed 60+ weekly assignments on statistical inference and ML. Focused feedback on reproducibility, metrics validation, and model-data fit.
AI/ML Research Assistant
VNR VJIET, India
- Fine-tuned multilingual NLP models (mBERT, IndicBERT) using LoRA and 8-bit quantization on PyTorch; improved access for 50,000+ farmers, reduced language barriers 40%, published reproducible benchmarks supporting government agricultural programs.
- Built ETL data pipelines (Python, Scrapy, SQL) with validation, drift detection, lineage tracking; generated 2M+ documents accelerating regional AI research 60%.
- Designed eval framework (BLEU, ROUGE, F1) with chaos tests and fallbacks; maintained 99.5% uptime across 6-month production pilot.
Featured Projects
Taylor: AI Career Stylist
RAG-powered LLM app automating resume/cover letter creation for 250+ users. 50% faster application processing, <3s latency, serverless architecture with personalized outputs.
GemmA.I: Vision Assistant
iOS accessibility app for blind/low-vision users with real-time scene understanding via MediaPipe Gemini. 100% on-device privacy, <500ms inference (INT8).
BetterLlama
Fine-tuned LLaMA 3.2 with LoRA/8-bit quantization. 1.8× faster inference, 99.9% MMLU accuracy, 4× compression, 60% cost reduction. Integrated chain-of-thought and LLM Guardrails.
ChessGPT
Custom tokenizer for move generation (~30 legal moves/prompt) with compact model footprint. GPT-based architecture for chess move prediction.
NJ Transit Assistant
1st Place HackRU. XGBoost delay prediction (500K+ records, 82% accuracy) + schema-aware RAG chatbot. 70% faster FAQ resolution, 15K+ queries processed.
Technical Stack
Core competencies across the AI development lifecycle
Let's build the future of AI together.
Currently open to research collaborations and engineering opportunities in generative AI and large-scale data systems. Based in United States.