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Data Science with less than a year in AI, Machine Learning & Data Analysis
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Data Scientist passionate about leveraging AI and machine learning to extract insights and build intelligent systems. Developed an AI-powered document extraction system using Mistral OCR and Groq Vision Language Models, a Retrieval-Augmented Generation (RAG) chatbot, and an AI data analysis agent. Proficient in Python, SQL, cloud platforms (AWS), and a wide range of ML/DL frameworks and tools. Dedicated to improving data accuracy, building scalable solutions, and identifying key performance indicators from diverse datasets.
University College of Science, Hyderabad
Master of Computer Applications (MCA)
N/A – June 30, 2023
St. Francis College for Women, Begumpet
BSc · Statistics
N/A – June 30, 2021
N-Labs AI
Data Scientist
September 1, 2024 – Present
Hyderābād, Telangana, India
Viai Solutions Pvt Ltd
Data Scientist Intern
July 1, 2024 – August 1, 2024
Hyderābād, Telangana, India
N-Extract - AI Document Processing & Extraction System
September 1, 2024 – June 1, 2026
Developed N-Extract, an AI-powered document extraction system using Mistral OCR and Groq Vision Language Models to extract structured data from invoices. Built dynamic prompt-based extraction pipelines to capture user-defined fields from varied document layouts and formats. Improved extraction accuracy to ~90% by separating header fields from complex line-item tables with specialized prompts for each section. Enhanced OCR quality via CLAHE-based contrast enhancement and image cropping, improving readability of handwritten and stamped documents. Developed a scalable serverless bulk-processing workflow using AWS Lambda and SQS for asynchronous extraction jobs. Integrated email-based document ingestion and generated structured JSON, Excel, and CSV outputs for downstream reporting. Used PandasAI to dynamically generate SQL queries and retrieve data for identified KPIs. Created automated visual reports with ECharts for intuitive business insights.
AI-Powered Document Intelligence Chatbot (RAG)
September 1, 2024 – June 1, 2026
Built a Retrieval-Augmented Generation (RAG) chatbot for querying PDFs, structured documents, and images using LangChain. Implemented Hybrid Search (BM25 + semantic vector search) for improved retrieval accuracy. Applied Recursive Character Text Splitting with overlap for efficient chunking and better context recall. Generated embeddings via Sentence Transformers, indexed in ChromaDB, with Claude Opus 4 for context-aware response generation. Developed backend APIs with Django to support real-time inference and dynamic document ingestion.
AI Data Analysis & Visualization Agent
September 1, 2024 – June 1, 2026
Built an AI data analysis agent using the OpenAI SDK and multi-step LLM workflows to identify relevant KPIs automatically from user datasets. Designed a dynamic KPI generation pipeline based on dataset structure, column semantics, and business context analysis. Used PandasAI to dynamically generate SQL queries and retrieve data for identified KPIs. Created automated visual reports with ECharts for intuitive business insights.
Predictive Maintenance using LSTM (Time-Series Modeling)
September 1, 2024 – June 1, 2026
Developed a deep learning LSTM model to predict Remaining Useful Life (RUL) of engines from multivariate sensor time-series data. Engineered sliding window sequences and applied normalization, improving model convergence and stability. Trained model with MSE loss + Adam optimizer, achieving accurate failure prediction by capturing temporal degradation patterns. Implemented real-time inference pipeline using FastAPI, enabling low-latency predictions for continuous monitoring systems.
Crop Recommendation System
July 1, 2024 – August 1, 2024
Built a crop recommendation model using soil type, nutrients, pH, temperature, and rainfall as input features. Achieved 80% accuracy with Random Forest and 90% accuracy with XGBoost. Developed front end using Flask, HTML, CSS with visual dashboards in Tableau. Deployed on Render for easy access by end-users.
Prompt Engineering for LLMs
DeepLearning.AI
January 1, 2025 – Present
AutoGen Multi-Agent Framework
DeepLearning.AI
January 1, 2025 – Present
MCP (Model Context Protocol) Course
Hugging Face
January 1, 2025 – Present
Specialized Program in Data Science
Scaler
January 1, 2024 – Present
Advanced SQL Certificate
HackerRank
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest in cutting-edge AI/ML applications, particularly in NLP, Generative AI, and MLOps. The diversity of projects (document extraction, RAG chatbot, predictive maintenance, crop recommendation) and the use of various modern tools and frameworks suggest a proactive and innovative approach, aligning well with a dynamic, tech-driven culture. The certifications further emphasize a commitment to staying current with industry trends.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving skills (e.g., improving OCR accuracy, dynamic KPI generation) and an ability to work with complex, multi-step workflows. The breadth of tools and frameworks used suggests adaptability and a continuous learning mindset. The focus on end-to-end solutions, from model development to deployment and monitoring, indicates a strong operational fit for roles requiring practical application of data science.