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Assessing your cultural and operational fit
AI Engineer with 1+ years in Python, Machine Learning, NLP, and Generative AI.
Enthusiastic fresher with strong expertise in Python, Machine Learning, NLP, and Generative AI, specializing in building end-to-end RAG-based systems and scalable ML solutions. Experienced in designing, developing, and deploying cloud-integrated applications with a proven ability to transform complex data into actionable insights using advanced analytical techniques. Passionate about LLMs, AI innovation, and solving real-world problems — with strong debugging, documentation, and structured development skills — eager to contribute to high-impact engineering teams.
Vardhaman College of Engineering, JNTUH
B.Tech · Information Technology
October 1, 2022 – May 1, 2026
Bank Customer Churn Prediction
November 1, 2024 – April 1, 2025
Machine learning solution to predict customer churn - Python, Scikit-Learn, Pandas; structured SQL-based data processing. • Engineered a machine learning model using Logistic Regression and Random Forest to proactively identify at-risk customers; applied structured data extraction and SQL-based transformation during preprocessing. • Conducted Exploratory Data Analysis (EDA) to identify key churn indicators — transaction patterns, account activity — with detailed analytical reporting and technical documentation. • Performed data preprocessing, feature engineering, and transformation; executed systematic debugging and testing cycles to ensure model accuracy and generalization.
RAG Document Processing & Chatbot System
January 1, 2024 – November 1, 2024
End-to-end RAG Document Processing and Chatbot System - Python, Streamlit, Elasticsearch, Sentence Transformers. • Built an end-to-end RAG pipeline using Streamlit and Elasticsearch with structured technical documentation maintained at each module for maintainability and debugging clarity. • Developed a real-time RAG-based chatbot using Gemini API for context-aware document querying; integrated PyMuPDF and Tesseract OCR - applied systematic debugging and unit testing throughout. • Implemented efficient document chunking, embedding, and SQL-style indexing strategies to improve data retrieval accuracy and query performance. • Designed automated pipelines to generate concise summaries of lengthy legal texts; documented all system components for long-term maintainability.
Python(Basics)
HackerRank
June 1, 2026 – Present
SQL Databases for Beginners
Udemy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate an interest in practical AI/ML applications (RAG systems, churn prediction) and a commitment to structured development practices. The publication record suggests an inclination towards research and innovation. While the projects are academic, they align well with the target role of an AI Engineer. The breadth of skills listed (Python, ML, RAG, LLM, Generative AI, Streamlit, Elasticsearch, Scikit-Learn, Pandas, SQL) indicates a foundational fit for an AI-centric environment. However, the lack of professional experience means cultural fit beyond technical alignment is largely speculative.
Soft Skills & Operational Fit
The candidate's project descriptions highlight a systematic approach to development, including debugging, unit testing, and technical documentation, which suggests good operational fit. The academic background and research publications indicate a strong drive for learning and problem-solving. However, without direct work experience, the ability to navigate complex team dynamics or handle high-pressure production environments is unproven.