📞 Phone: +91 8870377813
📧 Email: rajadhurai.rajendhiran21@gmail.com
💼 GitHub: Rajadhurairajendhiran123
🔗 LinkedIn: Rajadurai Rajendhiran

Passionate about AI, ML, DL, Computer Vision, Physical AI, and Computational Theory. Skilled in applying math, optimization, and technology to build innovative, real-world AI solutions.

iAgami Technologies | Mar 2024 – Present

• Spearheading AI research and development across Agriculture, Insurance, Talent Acquisition, Finance, and Business Intelligence domains.
• Worked on LLM fine-tuning and foundational model building for domain-specific applications.
• Built advanced systems using Information Retrieval, GNNs, and NLP-to-SQL pipelines.
• Integrated LLMs, AI agents, and multimodal retrieval systems for real-time Q&A and decision support.
• Developed transformer-based models, dashboards, and deployed scalable AI workflows.

PyTorch, Hugging Face Transformers, LangChain, Weights & Biases, PEFT (LoRA), GRPO, JAX, TRL

• Fine-tuned BERT, GPT using LoRA, GRPO, and SFT for Agri, Finance, and Insurance.
• Built foundational LLMs with advanced prompting and optimization pipelines.

LangChain, Neo4j, NetworkX, SentenceTransformers, Django REST API

• Created pipelines to convert NL queries into SQL.
• Combined vector search with KG traversal for RAG capabilities.

CLIP, ViT, FAISS, OpenCV, Hugging Face, Django REST API

• Developed image-text embedding search for harvest and insurance data.
• Exposed functionality via Django REST API endpoints.

CrewAI, AutoGen, LangGraph, Streamlit, Django

• Built multi-agent LLM frameworks for reporting, summarization, and BI insights.
• Orchestrated LLM workflows in real time.

DGL, PyG, Neo4j, RecBole, LightFM, Pytorch, JAX

• Designed hybrid recommenders using collaborative filtering + GNNs.
• Applied in talent-matching and HR analytics.

spaCy, AutoGen, LangGraph, LlamaIndex, PyMuPDF, Markdown Parser

• Converted resumes (PDF → Markdown) and matched profiles using LLM agents.
• Built backend with Django REST API.

Tools: PyTorch, JAX, Hugging Face, ScaNN, W&B, Hydra

• Domain-aligned model training using LoRA, GRPO, PPO.
• Designed scalable LLM architectures and RL fine-tuning loops.

Tools: LangGraph, AutoGen, CrewAI, FastAPI, Streamlit

• Built agents for parsing, Q&A, SQL generation, memory retention, and logic execution.

Tools: LlamaIndex, LangChain, FAISS, Neo4j

• Combined vector + KG-based search with PDF-to-Markdown parsing for retrieval tasks.

Tools: CLIP, ViT, OpenCV, Roboflow

• Enabled vision-text reasoning, object detection, OCR, and multimodal Q&A systems.

Tools: Neo4j, PyG, DGL, RecBole, LightFM

• Built KGs and used GNNs for recommendation, analytics, and intelligent matchmaking.

Python, R, Mojo, Java, C++, Julia, SQL, PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, LightGBM

Classification, Regression, Clustering, Deep Learning, Transfer Learning, GANs, Diffusion, RL, Few/Zero-shot, Self-Supervised Learning

Hugging Face, SpaCy, NLTK, OpenCV, YOLO, ViT, CLIP, MediaPipe, OCR, Text Summarization, RAG, BERT, GPT, DSPy, TextGrad

LangChain, LangGraph, AutoGen, CrewAI (flexible with any multi-agent systems)

FAISS, Semantic Search, Knowledge Graphs, Neo4j, GNNs (PyG, DGL), Recommenders (LightFM, RecBole)

FastAPI, Django, Docker, Git, GitHub Actions, ONNX, DVC, MLflow, Weights & Biases, Gradio

AWS (SageMaker, Lambda, Robomaker), GCP (Vertex AI, BigQuery), Azure ML, Azure IoT

Pandas, NumPy, Matplotlib, Seaborn, Excel, Power BI, Streamlit, Plotly

Sri Shanmugha College of Engineering and Technology
Expected Graduation: June 2026
B.Tech in Artificial Intelligence and Data Science

Relevant Courses:
Mathematics & Data Science (Python), OOPs, Algorithms and Data Structures I & II,
Data Analytics (PowerBI, Python), Data Visualization (Tableau, Matplotlib), Computational Theory