👋 Hi, I'm Ritesh Yennuwar, a Machine Learning and AI engineer currently pursuing a Master’s in Artificial Intelligence at Northeastern University (Roux Institute).

🔬 As an AI Developer, I thrive at the intersection of creativity and technology. My journey began with a fascination for artificial intelligence and its limitless potential to reshape our world. From developing intelligent learning companions to building sophisticated disease classification models, I'm dedicated to pushing the boundaries of what AI can achieve.

🏦 One of the systems I am building is an Automated Data Quality Management System (ADQMS) in collaboration with Bangor Savings Bank, where I developed the core module that automatically generates Great Expectations validation suites for datasets with 264M+ rows, replacing manual data validation workflows with a scalable AI-powered pipeline.

🤖 My interests lie in Machine Learning Engineering, LLM systems, MLOps, and scalable AI infrastructure. I enjoy designing systems that move beyond experimentation and enable reliable ML models to run in production environments.

🚀 Previously, I co-founded Happenix, where I served as Co-Founder & CTO, leading the development of a sports booking platform connecting players with ground owners through mobile applications. This experience strengthened my interest in building real-world software systems at scale.

💡 I enjoy working at the intersection of:

• Machine Learning Engineering
• Generative AI & LLM pipelines
• Data infrastructure & validation systems
• Scalable backend systems for AI applications

📚 When I'm not coding, I’m usually exploring new ML research, distributed training systems, and modern AI infrastructure tools.

Speech-to-Speech Translation Pipeline

Built a real-time system converting speech from one language to another using an ASR → Machine Translation → TTS pipeline.

Features

• GPU-accelerated inference
• Real-time microphone streaming
• Modular backend architecture

Technologies

PyTorch • Whisper ASR • MarianMT • Tacotron / Coqui TTS • Flask • Next.js

GitHub
https://github.com/RiteshYennuwar/real_time_voice_translation_backend

An AI-powered adaptive learning platform that personalizes study materials and quizzes using LLMs and vector search.

Features

• Context-aware content retrieval
• Semantic search with vector embeddings
• Student progress tracking

Technologies

Python • LLMs • Pinecone • Cohere API • Flask • MongoDB

GitHub
https://github.com/RiteshYennuwar/VITA

Deep learning system for medical image classification built for efficient deployment in limited-resource environments.

Features

• CNN-based classifier
• Experiment tracking with DVC
• Containerized inference pipeline

Technologies

TensorFlow • Keras • Docker • DVC • Flask

GitHub
https://github.com/RiteshYennuwar/ETE_Disease_Classification

• Machine Learning Engineering
• Generative AI Systems
• LLM Applications
• MLOps & Model Deployment
• Data Infrastructure & Quality Systems
• Scalable AI Platforms

Python • JavaScript

TensorFlow • PyTorch • scikit-learn • Hugging Face Transformers • OpenCV • NLTK

Pandas • NumPy • Great Expectations • DVC • Pydantic

Flask • Node.js • Express.js • Prisma

PostgreSQL • SQL Server • MongoDB • Redis

Docker • Git • GitHub Actions • VSCode