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AI Engineer with less than a year in Python, Generative AI & 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
To secure a position in a reputed organization where I can apply my skills in Python, Machine Learning, and AI to develop intelligent solutions and grow professionally.
Yogi Vemana University
Master of Computer Applications
August 1, 2023 – June 30, 2025
Sri vivekananda Degree College
Bachelor of Computer Science
August 1, 2020 – June 30, 2023
Document Intelligence System using RAG
June 24, 2026 – Present
Developed a Retrieval-Augmented Generation (RAG) system that extracts, indexes, and retrieves information from PDFs, DOCX files, and other unstructured documents. Used embedding models and vector databases such as FAISS or ChromaDB to perform semantic search and retrieve contextually relevant document sections. Integrated LLMs to generate accurate answers based on retrieved document content, reducing hallucinations and improving response reliability. Implemented document chunking, metadata storage, and contextual retrieval pipelines using frameworks like LangChain or LangGraph. Designed the system for enterprise use cases such as policy document querying, research assistance, and knowledge management chatbots.
Exploratory Data Analysis (EDA) with LLM Integration
June 24, 2026 – Present
Built an intelligent EDA system that automatically analyzes datasets, identifies missing values, detects outliers, and generates statistical summaries using Python libraries like Pandas, NumPy, Matplotlib, and Seaborn. Integrated Large Language Models (LLMs) to generate human-readable insights and explain trends, correlations, and anomalies in natural language. Implemented automated feature analysis including correlation heatmaps, distribution analysis, and categorical data visualization to assist in faster decision-making. Added a conversational interface where users can ask dataset-related questions such as “Which feature impacts sales the most?” and receive AI-generated explanations. Improved data analysis efficiency by reducing manual interpretation effort and enabling non-technical users to understand complex datasets easily.
Chatbot using n8n
June 24, 2026 – Present
Built an AI-powered chatbot workflow using n8n to automate conversations, API integrations, and backend processes without extensive coding. Integrated LLM APIs with messaging platforms such as WhatsApp, Telegram, or web-based chat interfaces to provide real-time responses. Created automated workflows for user query handling, database interaction, email notifications, and third-party API communication. Implemented conditional logic, memory handling, and webhook-based triggers to support dynamic and context-aware conversations. Improved operational efficiency by automating repetitive support tasks and enabling scalable conversational AI solutions for business workflows.
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in applying AI to practical problems, such as document intelligence, data analysis, and chatbots. This aligns well with an innovative and solution-oriented culture. The diversity of projects, from RAG systems to EDA with LLM integration and n8n chatbots, suggests a broad interest in AI applications. However, the candidate's experience level is 0, and all projects are personal, which might indicate a need for mentorship and structured team environments to fully integrate into an enterprise culture.
Soft Skills & Operational Fit
The candidate lists Analytical Thinking, Problem Solving, Communication, Team Collaboration, and Quick Learner as soft skills. These are generally positive attributes for an AI Engineer role, indicating a potential for effective problem-solving and teamwork. However, without specific examples or interview data, the depth of these skills cannot be fully assessed.