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Generative AI Engineer with less than a year in RAG pipelines & LLM development.
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Assessing your cultural and operational fit
Motivated and detail-oriented Generative AI Engineer fresher with hands-on project experience in RAG pipelines, agentic AI workflows, prompt engineering, and LLM-based application development. Skilled in Python, OpenAI API, LangChain, LangGraph, Streamlit, FAISS, ChromaDB, SQL, and AWS services including S3 and Bedrock. Built AI-powered document question-answering systems, conversational chatbots with memory and tool integration, and machine learning solutions for real-world use cases. Strong understanding of semantic search, embeddings, vector databases, transformer concepts, tokenization, attention mechanism, and model evaluation. Passionate about developing scalable, user-focused AI applications and eager to contribute as a GenAI Engineer, LLM Engineer, or AI Engineer.
GenAI Document Q&A Chatbot using RAG
June 1, 2026 – Present
Developed a Retrieval-Augmented Generation (RAG) system to answer questions from custom PDF documents with accurate and context-aware responses. Implemented embeddings-based semantic search using FAISS and ChromaDB to retrieve relevant chunks before generating answers. Integrated OpenAI APIs with LangChain to improve answer quality, relevance, and reduce hallucinations. Designed an interactive workflow for document upload and real-time query processing.
Agentic AI Chatbot with Memory and Tool Integration
June 1, 2026 – Present
Built a conversational chatbot with memory using LangChain and LangGraph for better context retention across user interactions. Implemented agentic workflows for multi-step reasoning and dynamic decision-making. Integrated external tools such as search and calculator APIs to enhance chatbot functionality. Developed Python-based backend logic to support real-time conversations and prompt optimization.
Plant Disease Detection and Multi-Language Solution
June 1, 2026 – Present
Developed a CNN-based image classification model to detect 38 plant diseases with 97% accuracy. Built a web-based real-time image upload and prediction system with multilingual output in English, Hindi, and Tamil. Evaluated model performance using confusion matrix, precision, recall, and F1-score.
Python
Unknown
June 1, 2026 – Present
SQL
Unknown
June 1, 2026 – Present
Pandas
Kaggle
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a breadth of interest within AI, from GenAI applications to traditional ML (plant disease detection). The stated openness to various AI/LLM roles suggests adaptability. The focus on practical application in projects indicates a results-oriented mindset. However, without information on collaboration or problem-solving styles, a deeper cultural fit analysis is not possible.
Soft Skills & Operational Fit
The candidate's resume indicates a motivated and detail-oriented individual with a passion for developing scalable, user-focused AI applications. The project descriptions suggest an ability to work on complex technical challenges. However, without specific assessment data on communication, logical reasoning, or teamwork, a comprehensive evaluation of soft skills and operational fit is limited.