Building production-grade AI systems — fine-tuned LLMs, RAG pipelines, and scalable model serving on AWS. I turn raw data into intelligent, deployed solutions that reduce latency, improve accuracy, and ship to production.
Production LLM pipelines with advanced prompt engineering (few-shot, chain-of-thought, instruction tuning), RAG via FAISS vector databases, fine-tuned transformer models on industry-specific training datasets, and full evaluation framework with A/B testing and automated benchmarking.
Accuracy+30%
Efficiency+40%
Latency−35%
Reliability+25%
02RAGGenAI
RAG & Generative AI Systems
Production-ready RAG systems with semantic document search and generative Q&A. Dense vector retrieval via FAISS combined with transformer-based generation for accurate, grounded responses at scale.
RetrievalSemantic
InterfaceGradio
03LLM
Python Syntax Correction Tool
Interactive code correction powered by StarCoder2-7B and Llama-3.1-8B. Accepts buggy Python code and returns corrected output via real-time model inference using Hugging Face Transformers and Accelerate.
Models2 LLMs
SpeedReal-Time
04Computer Vision
Vehicle Detection & Speed Tracking
Real-time vehicle detection and speed estimation from video feeds using YOLOv8 and OpenCV. Computes speed via frame-to-frame displacement measurement and logs vehicle ID, speed, and timestamps for traffic analysis.
DetectionReal-Time
ModelYOLOv8
05ML · Finance
Stock Market Analysis Platform
AI-powered stock analysis platform with real-time financial data, RSI/SMA technical indicators, news sentiment analysis via OpenAI API, and YouTube video insights. Full interactive visualization dashboard.
APIs4 Sources
AnalysisReal-Time
06IoT · Safety
RF & Alcohol Detection Safety System
Real-time road safety system using RF and alcohol sensors to detect speeding and intoxicated driving simultaneously. Developed alert mechanisms for immediate driver and authority notification to enhance road safety.
SensorsMulti
ResponseReal-Time
Side Project
🏗️ AWS IoT Data Pipeline
End-to-end sensor data pipeline on AWS Free Tier — IoT Core → S3 → Glue → SageMaker with XGBoost, SHAP explainability, and 24h forecasting.
Architected end-to-end LLM pipelines — improved response accuracy ~30% via few-shotchain-of-thoughtinstruction tuning, RAG with FAISS vector databases, and Hugging Face fine-tuning on industry-specific training datasets.
Designed scalable data pipelines boosting preprocessing efficiency 40% — Python, pandas, NumPy; integrated modular workflows for dataset versioningbatch processing and reproducible experimentation.
Engineered high-performance model serving infrastructure reducing inference latency 35% via FastAPImodel quantizationbatching strategies and optimized request handling for real-time production workloads.
Built LLM evaluation & monitoring framework improving reliability 25% — A/B testingBLEU/ROUGEsemantic similarity metrics with automated benchmarking and statistical significance testing.
AI Engineer Intern
InternshipAug 2025 – Oct 2025 · 3 mos
Developed LLM-powered features and fine-tuned models on domain-specific datasets to improve accuracy and reasoning capabilities.
Built workflows for data preprocessing, model training, evaluation, and continuous model improvement pipelines.
Conducted research on emerging LLM techniques and collaborated cross-functionally to implement AI-driven solutions across teams.
Background
Education & Certifications
New York Institute of Technology
MS · Computer Science · New York, NY
Analysis of Algorithms · Database · Machine Learning · Deep Learning · Artificial Intelligence
R.M.K Engineering College
BE · Electronics & Communication · Chennai, India
Programming · Data Structures · Microcontrollers · Digital Electronics · VLSI Design
Python for Data Science — edXProgramming for Everybody — CourseraAI For Everyone: Master the Basics — edX🏆 3rd Place · Paper Presentation🏆 Round 3 Finalist · IICC — AICTE & Coding Ninjas
Let's Build Something.
Open to AI Engineer, ML Engineer, and LLM Engineer roles. Especially interested in production AI systems, AWS-native ML, and LLM infrastructure.