Welcome to my portfolio
Jhanavi Putcha
Machine Learning Engineer | Data Scientist
Building AI-driven solutions that transform data into measurable business impact—from computer vision systems processing 30M+ daily inspections to production-grade ML models.
About Me
I'm a Machine Learning Engineer with a Master's degree in AI from the University at Buffalo, focused on building intelligent systems that solve real-world problems at scale. My expertise spans computer vision, deep learning, and end-to-end ML pipeline development—from data preprocessing to production deployment.
I've worked on AI solutions ranging from industrial inspection systems processing millions of daily items to market segmentation models driving business strategy. I thrive at the intersection of research and application, leveraging tools like TensorFlow, PyTorch, and AWS to deliver measurable outcomes.
Deep Learning
Building neural networks for computer vision, NLP, and predictive analytics
Data Engineering
Processing and analyzing large-scale datasets for actionable insights
ML Deployment
Deploying production-ready models with AWS and cloud infrastructure
Technical Skills
Languages
Frameworks & Libraries
Tools
Cloud (AWS)
Experience
Project Eagle Eyes – Vision AI for Industrial Printing
Nissha Medical Technologies
- •Architected an AI-driven inspection system to detect and analyze Q-Block defects in ultra-high-speed ticket printing lines processing AI-driven 30M+ tickets daily
- •Trained a YOLOv8-based object detection model achieving high accuracy in localizing large and small Q-Blocks from production images
- •Developed color fading and area-based quality analytics by extracting pixel-level features (HEX/RGB, dimensions) from detected regions
- •Built visualization pipelines to track color degradation trends, enabling early detection of machine wear and reducing downtime
- •Implemented automated GOOD/NOT GOOD classification system, significantly reducing manual inspection dependency and improving defect detection consistency automated GOOD/NOT GOOD classification
Artificial Intelligence Intern
AVR Research and Development Pvt. Ltd.
- •Executed comprehensive data pre-processing and cleaning procedures, enhancing dataset quality and reducing model training time by 25%
- •Designed and implemented ML models using diverse algorithms (decision trees, random forests, SVMs, neural networks), achieving 15% improvement in predictive accuracy
- •Conducted in-depth model evaluation and hyperparameter tuning, optimizing performance across multiple deployments
- •Successfully deployed models into production, contributing to data-driven decision-making and measurable business impact production-grade ML
Machine Learning Intern
Feynn Labs
- •Developed ML algorithms for market segmentation , enabling targeted marketing strategies and improving customer engagement through high-value segment identification
- •Collaborated with cross-functional teams to gather, clean, and preprocess large-scale datasets, ensuring data integrity for model training
- •Leveraged data analysis techniques to uncover actionable business insights, directly influencing product positioning and customer acquisition strategies actionable business insights
Featured Projects
A selection of projects showcasing my work in ML, computer vision, and AI applications
Music Generator using Genetic Algorithm
Problem
Most music generators depend on large datasets, making it hard to personalize output without retraining.
Solution
Built a melody generator that evolves music using a genetic algorithm and improves based on iterative selection (fitness).
Outcome
Generates evolving melodies with controllable variation and selection-driven improvement.
Comparative Analysis of RL Algorithms (Discrete & Continuous)
Problem
Choosing the right RL algorithm depends heavily on the action space and environment dynamics.
Solution
Implemented and compared PPO, DQN, DDQN, and A2C across discrete and continuous action-space settings.
Outcome
Analysis highlights trade-offs in stability, sample efficiency, and performance by action space.
Crop Prediction using ThingSpeak
Problem
Farmers need data-driven crop recommendations based on real-time environmental conditions.
Solution
Built a crop prediction workflow using ThingSpeak sensor data (temperature, pH, rainfall, humidity) to guide crop selection.
Outcome
Improved crop selection decisions using real-time sensor-driven insights.
Surya Namaskar Trainer
Problem
Beginners struggle to learn proper Surya Namaskara (Sun Salutation) yoga poses without real-time guidance, leading to incorrect postures and potential injury.
Solution
Built an interactive platform that uses computer vision to provide real-time feedback on yoga pose correctness and alignment.
Outcome
Published research paper (DOI: JETIR2404467) demonstrating effective pose correction for yoga practitioners
Warehouse Robot using Q-Learning and SARSA
Problem
Warehouse logistics require efficient automated systems for picking up and delivering parcels between locations.
Solution
Developed a reinforcement learning agent using Q-Learning and SARSA algorithms to autonomously navigate warehouse environments.
Outcome
Agent successfully learns optimal paths for parcel delivery in complex warehouse grid environments
AI Recipe Generator
Problem
Users often have ingredients but lack inspiration or knowledge to create recipes, especially from visual input.
Solution
Created an AI-powered application that generates food recipes from both image and text inputs, featuring an interactive chatbot powered by OpenAI.
Outcome
Delivers personalized recipe recommendations based on available ingredients through natural conversation
Achievements & Recognition
ML Speaker
Delivered an engaging machine learning-focused talk to an audience of 200+ students , sharing insights on AI/ML concepts and career paths. Received a Letter of Appreciation for knowledge sharing and presentation excellence.
ML Competition Winner
Secured 3rd place in a competitive ML challenge by developing a production-grade AI solution, demonstrating strong problem-solving skills and ability to deliver practical, deployable models under pressure.
Institutional Interface Developer
Built a comprehensive platform that streamlined assignment submissions and improved academic workflows for the institution, showcasing full-stack development capabilities and user-centric design thinking.
Get In Touch
I'm always open to discussing new opportunities, AI/ML projects, or just connecting with fellow tech enthusiasts.
jhanaviputcha957@gmail.com
linkedin.com/in/jhanavi-putcha
GitHub
github.com/Jhanavi-24
Phone
+1 (716) 957-9522
Location
Buffalo, NY, USA