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Generative AI Engineer with less than a year in LLM Applications & RAG Systems.
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Generative AI Engineer with hands-on experience building production-ready LLM applications, Retrieval-Augmented Generation (RAG) systems, and multimodal AI solutions. Delivered 3 end-to-end deployed AI systems across legal, media, and multimodal domains. Skilled in LangChain, FAISS, ChromaDB, HuggingFace, and Docker-based deployment. Experienced in agentic AI workflows, prompt engineering, and scalable cloud deployment. Actively seeking Generative AI / LLM Engineer roles (0-2 yrs).
IIT-M GUVI
Advanced Programming Professional & Master Data Science · Large Language Models (LLMs), RAG Systems, MLOps, Generative AI, Deep Learning, Data Engineering, Python Programming
February 1, 2024 – October 1, 2024
Paavai College of Engineering
B.E. Electronics & Communication Engineering · Data Structures & Algorithms, Python Programming, Signal Processing, Communication Systems, Microprocessors
August 1, 2019 – April 1, 2024
Legal Multi-Agent RAG System
October 1, 2025 – June 1, 2026
Architected a multi-agent RAG pipeline for legal Q&A; using Llama-3, LangChain orchestration, and a citation validation agent. Indexed and retrieved across 12+ legal documents using FAISS vector store and ChromaDB with metadata filtering, achieving sub-3s query response. Improved answer relevance by ~35% through optimised chunking strategy and semantic metadata filtering vs. naive retrieval baseline. Reduced LLM hallucinations by ~40% by implementing a grounding-based citation validation layer before response generation. Deployed interactive Gradio web interface enabling real-time legal querying with source citation display.
View ProjectPalludagam AI – Multimodal LLM System
June 1, 2025 – June 1, 2026
Built production-ready multimodal AI app processing 3 input types (image, audio, PDF) via Gemini 2.5 Flash API integration. Reduced Docker image size by ~40% using python:3.12-slim base, cutting cold-start time and cloud deployment costs. Automated CI/CD pipeline via Docker Hub (rajann71/palludagam-ai-hub:v1), enabling one-command reproducible global deployment. Designed real-time Streamlit UI with consistent cross-environment performance and zero "works on my machine" issues.
View ProjectGPT-2 Fine-Tuning & Deployment
March 1, 2025 – September 1, 2025
Fine-tuned GPT-2 transformer model on a custom domain dataset achieving stable convergence within 5 epochs and ~30% improved text generation quality. Built custom tokeniser and preprocessing pipeline; optimised hyperparameters (learning rate, batch size, warmup steps) for best validation loss. Deployed production-ready text generation web app on Streamlit with real-time inference and user-configurable generation parameters.
View ProjectAI-Powered Daily News Brief Generator
January 1, 2025 – July 1, 2025
Built transformer-based summarisation system processing 50+ articles/day across 5 topic categories with personalised filtering. Integrated 3 real-time external news APIs to stream and process live data with automated deduplication and relevance ranking. Deployed interactive Streamlit application with topic-preference UI, reducing reading time by an estimated 70% per user session.
View ProjectSingapore Resale Flat Price Prediction
November 1, 2024 – May 1, 2025
End-to-end regression ML pipeline; feature engineering, model validation, and Streamlit deployment. Achieved R2 score of 0.91 on test set.
View ProjectIndustrial Copper Price & Sales Modelling
October 1, 2024 – April 1, 2025
Random Forest regression for industrial price forecasting; outlier treatment, skew correction and feature selection — improved RMSE by ~22% vs baseline.
View ProjectPhonePe Pulse Data Visualisation
September 1, 2024 – March 1, 2025
Analysed 1M+ transaction records; EDA, trend analysis, and business intelligence dashboards for payment pattern insights.
View ProjectCultural Fit Analysis
The candidate's project portfolio is highly aligned with the 'Generative AI Engineer' target role, showcasing a deep interest and practical application in LLMs, RAG, and multimodal AI. The diversity of projects (legal Q&A, news brief, multimodal app) indicates adaptability and a broad interest within the AI domain. The academic background, including a Master's specialization in LLMs and Generative AI, further reinforces this fit. However, the lack of professional experience means cultural fit in a corporate setting is yet to be proven.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a results-oriented approach, focusing on quantifiable improvements (e.g., reduced Docker image size, improved answer relevance, reduced hallucinations, improved RMSE). The emphasis on 'production-ready' and 'real-time' applications suggests an understanding of operational requirements and user experience. The academic nature of all projects means direct professional soft skill demonstration is limited, but the project outcomes imply problem-solving and execution capabilities.