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Bhavya Chikkala

Full-Stack Software Engineer • Eluru, India • b****************@gmail.com • 939****364 • bhavyachikkala/••••• • drivetube.ai/•••••

Professional Summary

Full-Stack Software Engineer with 0 years of experience building web applications and machine-learning prototypes using JavaScript (React, Node, Express) and Python (Flask, scikit-learn). Delivered a full-stack receipt scanner using Tesseract.js OCR with a React frontend, Node/Express REST APIs and MongoDB; implemented analytics with Chart.js and ExcelJS and prepared exports for Power BI and Tableau. Built an OpenCV-based face recognition attendance system with Flask and SQLite, and developed ML classifiers for email spam detection using Pandas, NumPy and scikit-learn (94% accuracy). Completed an LLM post-training internship focused on prompt engineering, supervised fine-tuning and RLHF data pipelines. Skilled in designing RESTful APIs, database schemas (MongoDB, MySQL, SQLite) and CRM automation in Salesforce. Seeking an entry-level full-stack role to deliver production-ready features that combine web and AI components.

Technical Skills

Programming Language: Python,SQL
Frontend Technologies: HTML,CSS,React,Chart.js,Bootstrap
Backend Technologies: Node.js,Express.js,Flask
Databases: MongoDB,MySQL,SQLite
API & Integrations: RESTful APIs
Data Analysis & Visualization: Pandas,NumPy,Tesseract.js,Power BI,Tableau
AI/ML Frameworks & Libraries: scikit-learn,OpenCV
Generative AI & LLMs: Fine-Tuning,RLHF
Enterprise Platforms: Salesforce
Platform Development & Configuration: Salesforce Flow,Process Builder

Work Experience

Ethara AI
Remote
LLM post training Intern
Feb 2026 – Apr 2026
Worked on post-training data pipelines and evaluation to improve LLM response quality at an AI company focused on generative models.
Tech Stack: Python, Supervised Fine-Tuning, RLHF, Data annotation
  • Processed structured datasets and designed annotation schemas to standardize prompt-response examples for supervised fine-tuning (SFT).
  • Implemented prompt variations and conducted response validation to identify failure modes in LLM outputs for targeted improvement.
  • Conducted data annotation workflows and quality checks to produce labeled prompt-response pairs suitable for RLHF training.
  • Developed Python scripts to sample, filter and format training examples for SFT pipelines, improving dataset consistency for downstream training.
  • Documented prompt guidelines and annotation rubrics to accelerate reviewer onboarding and reduce labeling inconsistencies.
  • Prepared cleaned and validated datasets for handoff to engineering teams for fine-tuning and evaluation cycles.
SmartInternz
Virtual
Salesforce Developer Virtual Intern
Mar 2023 – May 2023
Delivered CRM customizations and automation using Salesforce to support business workflows, reporting and stakeholder decision-making.
Tech Stack: Salesforce, Salesforce Flow, Process Builder, Power BI, Tableau
  • Developed custom Salesforce objects, page layouts and validation rules to model sales and service workflows.
  • Implemented automation using Salesforce Flow and Process Builder to replace manual tasks and streamline approvals.
  • Built interactive Salesforce dashboards and reports to surface key sales KPIs and operational metrics for business users.
  • Configured report types, scheduled reports and dashboard refreshes to support recurring business reviews.
  • Prepared CRM data exports and CSVs for ingestion into Power BI and Tableau to enable deeper cross-system analytics.
  • Documented deployment steps and supported user acceptance testing and training for delivered automations.

Projects

AI-Powered Expense Receipt Scanner
Tools Used: React.js, Node.js, Express.js, MongoDB, Tesseract.js, Chart.js, ExcelJS, HTML, CSS, Bootstrap, REST APIs
  • Developed a full-stack receipt scanning application with a React frontend and Node/Express REST API to extract vendor, amount and date using Tesseract.js OCR.
  • Implemented backend parsing and persistence in MongoDB to store extracted receipt records and support user queries.
  • Built analytics dashboards with Chart.js to visualize spending trends and category breakdowns for end users.
  • Implemented automatic expense categorization using rule-based heuristics and backend preprocessing (85% categorization accuracy).
  • Enabled one-click Excel export using ExcelJS and prepared CSV/Excel exports compatible with Power BI and Tableau for deeper analysis.
AI Smart Attendance System
Tools Used: Python, Flask, OpenCV, SQLite, ExcelJS
  • Built a face-recognition attendance system using OpenCV for real-time identification and Flask to serve the dashboard.
  • Implemented SQLite-backed storage for attendance records and secure retrieval endpoints for the dashboard.
  • Developed a Flask-based dashboard to show attendance history and to generate automated reports.
  • Automated report generation and Excel export to allow administrators to download attendance logs.
  • Integrated face detection preprocessing to improve recognition reliability under varied lighting conditions.
Email Spam Detection (Machine Learning)
Tools Used: Python, Pandas, NumPy, scikit-learn, SQL, MySQL
  • Performed data cleaning and preprocessing on email datasets using Pandas and SQL to extract features relevant to spam classification.
  • Engineered text features and applied TF-IDF vectorization to represent message content for model training.
  • Trained and evaluated Naive Bayes and Random Forest classifiers using scikit-learn and selected models based on cross-validation.
  • Optimized feature selection and model parameters to achieve 94% classification accuracy on held-out test data.
  • Implemented pipeline scripts to load, preprocess and persist model inputs and outputs for reproducible experiments.

Education

Sir C.R. Reddy College of Engineering, Eluru
Bachelor of Technology in Computer Science and Engineering • Eluru, India • 2021 – 2025

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