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Venkatesh Panguluri

AI & Machine Learning Engineer • Visakhapatnam, India • p********************@gmail.com • +91*******019 • linkedin.com/••••• • github.com/•••••

Professional Summary

AI & Machine Learning engineer (B.Tech AI & ML) with hands-on internship experience building end-to-end ML systems, computer vision models, and automation workflows. Proficient in Python, Scikit-learn, TensorFlow, Flask and workflow automation using n8n; experienced in data preprocessing, feature engineering, model evaluation and deploying production-ready applications on Render. Seeking an entry-level ML engineer role where I can contribute model development, deployment, and automation expertise to deliver data-driven features.

Technical Skills

Programming Languages: Python,C
Web Technologies: REST APIs
Frameworks and Libraries: Scikit-learn,TensorFlow,Keras,Pandas,NumPy,Seaborn,Flask
Databases: SQL,MySQL
Cloud and DevOps: Render,Model Deployment
Data and Analytics: Feature Engineering,Supervised Learning,Regression,Classification,Random Forest,Jupyter Notebook,Data Preprocessing
Tools and Methodologies: Git,GitHub
Deep Learning & Computer Vision: Convolutional Neural Networks,Transfer Learning
Web, APIs & Automation: Webhooks,n8n,Google Calendar API

Work Experience

Hemambar Financial Solutions
Visakhapatnam
Software Developer Intern
Dec 2025 – Mar 2026
Tech Stack: n8n, Google Calendar API, REST APIs, Webhooks, OAuth, Workflow Automation
  • Designed and developed an AI-powered calendar chatbot using n8n and Google Calendar API to automate scheduling, event creation, and reminders, enabling consistent automated scheduling flows.
  • Integrated REST APIs and custom webhooks to connect internal services and third-party calendar providers, improving end-to-end workflow reliability and interoperability.
  • Built reusable n8n workflows for scheduling logic, conflict resolution, and notification rules to reduce manual scheduling steps and streamline operations.
  • Implemented robust error handling, logging, and retries for webhook flows and API calls to improve observability and simplify incident diagnosis.
  • Collaborated with product stakeholders to gather scheduling requirements and translated them into technical automation flows, test cases and user-facing behavior.
  • Deployed and validated chatbot workflows in staging and supported production rollout, including secure Google OAuth scopes and access configuration.
SmartBridge Technologies
Remote
AI/ML Virtual Intern
Jul 2025 – Aug 2025
Tech Stack: Python, Scikit-learn, Pandas, NumPy, Jupyter Notebook, Model Evaluation
  • Developed supervised ML models in Python using Scikit-learn for predictive analytics tasks, focusing on reproducibility and baseline performance.
  • Performed end-to-end data preprocessing and feature engineering with Pandas and NumPy, including missing-value handling, scaling and transformations.
  • Trained and evaluated classification and regression models using cross-validation and standard metrics to select robust algorithms.
  • Optimized model performance through hyperparameter tuning and iterative feature selection to improve generalization.
  • Packaged models into deployment-ready artifacts (serialization and inference scripts) for integration with web applications.
  • Prepared technical documentation and presented model results and recommendations to mentors and project stakeholders.

Projects

AI-Based Crop Advisory and Management System
Tools Used: Flask, Scikit-learn, TensorFlow, Random Forest, Convolutional Neural Networks, Render, GitHub
  • Developed a Flask web application integrating a Random Forest model for crop recommendation and a CNN for plant disease detection to provide actionable farming advice.
  • Implemented multilingual support, fertilizer recommendations and weather forecasting integration to make the tool practical for field use.
  • Deployed the application on Render with GitHub integration to enable secure, production-ready hosting and continuous updates.
Electric Motor Temperature Prediction
Tools Used: Flask, Scikit-learn, Pandas, NumPy, MinMaxScaler, Render
  • Built a Flask-based ML application to predict electric motor temperature using operational sensor data and regression modeling.
  • Performed rigorous data preprocessing including scaling with MinMaxScaler and feature engineering to improve model input quality.
  • Deployed the model on Render and designed a responsive UI for real-time inference and monitoring.
AI-Powered Calendar Chatbot (Project)
Tools Used: Python, n8n, Google Calendar API, REST APIs, Webhooks
  • Implemented an intelligent scheduling assistant using n8n nodes and Google Calendar integration to automate event creation and reminders.
  • Automated end-to-end workflows that handle scheduling conflicts, reminders and follow-up actions via REST APIs and webhooks.
  • Tested and validated flows to ensure reliable operation and created documentation for integration and usage.

Education

U.V. Patel College of Engineering, Ganpat University
Bachelor of Technology – Computer Engineering (AI & ML) – CGPA: 7.8 / 10 • Gujarat, India • 2022 – 2026

Certifications

Machine Learning with Python — IBM SkillBuild
Data Analysis with Python — IBM Cognitive Class
Deep Learning Fundamentals — IBM Cognitive Class
Machine Learning — SmartBridge Technologies

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