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AI Engineer with less than a year in LLMs & Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring Data Scientist and AI Engineer with expertise in LLMs, NLP, and machine learning. Experienced in building scalable, data-driven applications and deploying AI solutions for real-world impact.
COIMBATORE INSTITUTE OF TECHNOLOGY
M.Sc. · Data Science
August 1, 2022 – Present
PROFITSTORY.ai
LLM INTERN
June 1, 2025 – November 1, 2025
India
SECURE-HEALTH (AI-Powered Secure Healthcare Prediction System)
June 27, 2026 – Present
Built a privacy-preserving ML system for endometriosis risk prediction using Flask, enabling secure client-server communication for healthcare data. Implemented Fully Homomorphic Encryption (Concrete ML, FHE) with XGBoost to perform encrypted inference without exposing sensitive patient data. Achieved 94.6% prediction accuracy using the optimized XGBoost model for reliable risk classification. Tools/Techniques: Python, Flask, Concrete ML, XGBoost, Fully Homomorphic Encryption (FHE), PBKDF2-SHA256
FIN_MANIFESTO (Graph RAG Financial Intelligence System)
June 27, 2026 – Present
Built a Graph RAG system unifying 303 SEC filings across 41 companies into a knowledge graph for financial analysis and benchmarking. Designed a Neo4j + Qdrant hybrid retrieval system with CrewAI and LLaMA 3.3 for semantic and graph-based query resolution. Developed a full-stack application using FastAPI and React with real-time graph visualization using NetworkX. Tools/Techniques: Python, Neo4j, Qdrant, CrewAI, Docling, FastAPI, React, GraphRAG, Docker
INFOBOT(RAG-Based Intelligent Chatbot System)
June 27, 2026 – Present
Developed an AI-powered chatbot using RAG (Retrieval-Augmented Generation) and advanced NLP techniques to deliver fast and accurate responses. Integrated FAISS vector database for efficient similarity search, improving retrieval speed and contextual relevance of answers. Achieved 93% accuracy in response generation by combining GenAI-based retrieval with optimized vector search pipelines. Built an interactive user interface using Streamlit, enabling seamless user experience for text and speech-based interactions. Tools/Techniques: FAISS, RAG, Gen AI, EasyOCR, Vosk, Python, NLP, Speech Recognition, Streamlit.
Cultural Fit Analysis
The candidate's academic projects showcase a diverse range of applications for AI, from healthcare to finance and general chatbots, indicating adaptability and a broad interest in AI's potential. The hackathon win and club involvement suggest a proactive and collaborative attitude, which generally aligns well with a dynamic technical culture. The candidate's profile indicates a strong drive for learning and applying cutting-edge AI technologies.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving mindset and an ability to work on complex, multi-faceted AI systems. Participation in a hackathon and a leadership role in a club suggest teamwork and initiative. The focus on real-world impact in project descriptions aligns with operational fit for an AI Engineer role.