About Me
Building Intelligent, Scalable AI Infrastructure
I am an entry-level Machine Learning Engineer specializing in Computer Vision, Document AI, and Multimodal AI systems. I have a passion for design and implementation of high-throughput, data-intensive pipelines that process, validate, and extract structured intelligence from complex unstructured sources.
My experience spans training transformer-based vision models, orchestrating multi-stage data pipelines with Apache Airflow and Vertex AI, fine-tuning large language models (LLMs) via LoRA/PEFT, and building optimized, production-ready inference endpoints.
02 / Technical Skills
Core Competencies & Toolchain
Machine Learning & Core
Computer Vision & Document AI
NLP & Large Language Models
Model Optimization & Systems
Backend, Cloud & Dev
03 / Professional Experience
Journey So Far
Machine Learning Intern
Intrust Innovation Labs- Developed large-scale Document AI pipelines using Google Cloud Vertex AI to process scanned business documents and extract structured information from unstructured PDFs.
- Built OCR-driven document processing workflows encompassing document ingestion, preprocessing, validation, deduplication, quality assurance, and structured output generation.
- Designed automated data curation pipelines, enhancing document extraction accuracy and significantly reducing manual processing overhead.
- Developed transformer-based computer vision models using PyTorch, optimizing classification accuracy through transfer learning, advanced data augmentation, and hyperparameter search.
- Integrated deep learning models into production inference workflows via FastAPI/REST APIs and backend services.
- Orchestrated multi-stage machine learning workflows using Apache Airflow to automate training data generation and model deployment cycles.
04 / Selected Projects
Innovative AI Implementations
FarmSage Multimodal Platform
End-to-End AI Platform for Precision Agriculture
A comprehensive agricultural decision-support platform combining deep-learning computer vision models with real-time generative conversational intelligence.
Key Achievements & Architecture
- Developed a multilingual crop-disease detection and advisor system, combining a Gemini API RAG pipeline with live local weather forecasts.
- Designed an end-to-end vision system using YOLO for object localization and Swin Transformer for fine-grained leaf disease classification, achieving 95% test accuracy.
- Implemented robust training optimizations including Mixed Precision (AMP), OneCycleLR scheduling, AdamW optimization, gradient clipping, and advanced augmentations (MixUp, Random Erasing).
- Optimized model serving latency by 30% by exporting weights to ONNX runtimes served via FastAPI and Cloudflare Workers.
Aura Conversational Assistant
Quantized LLM Fine-Tuning & Deployment Workflow
An optimized conversational agent trained on customized domain data using advanced parameter-efficient fine-tuning (PEFT) and low-bit quantization.
Key Achievements & Architecture
- Fine-tuned LLaMA 3.2B with 4-bit precision quantization and LoRA adapters, building an end-to-end model training, evaluation, and logging pipeline.
- Designed dataset preparation, tokenization, model validation, and prompt-refinement scripts, reducing memory footprints during training.
- Developed a lightweight Flask REST API supporting real-time text-to-speech and speech-to-text response features with low latency.
- Managed end-to-end production pipelines including model pruning, serialization, and Docker containment.
05 / Research & Publications
IEEE Published Research
"Farm Sage: Multilingual Agricultural Q&A Assistant and Plant Leaf Disease Detection"
Presented at the 2025 10th International Conference on Smart Structures and Systems (ICSSS)
Chennai, India, 2025 · pp. 1–6
This research paper presents an end-to-end multimodal AI platform that combines object localization (YOLO) and fine-grained classification (Swin Transformer) to identify plant leaf diseases with 95% accuracy, integrated with a weather-aware, multilingual Retrieval-Augmented Generation (RAG) assistant for crop care.
Academic Education
Education Background
Bachelor of Technology (B.Tech)
Artificial Intelligence and Data Science
K.S. Rangasamy College of Technology
Tiruchengode, Tamil Nadu, India
Machine Learning, Data Structures & Algorithms, Deep Learning, Statistical Analysis, Database Management Systems.