Vivek Tippireddy

Description: An end-to-end RAG pipeline that combines Gemma2 embeddings with Weaviate vector database for intelligent document retrieval and answer generation.

Key Features:

• Document ingestion and intelligent chunking
• Gemma2 embedding generation
• Weaviate vector database integration
• Advanced retrieval logic with relevance scoring
• FastAPI deployment with modular architecture
• Performance evaluation and monitoring dashboards

Technologies: Ollama, Gemma2, Weaviate, FastAPI, Python, Docker

Description: A hybrid recommendation system that combines collaborative filtering with content-based approaches to provide personalized restaurant recommendations.

Key Features:

• Collaborative filtering using user-restaurant interactions
• Content-based filtering with TF-IDF and cosine similarity
• Automated data ingestion and processing pipeline
• Django REST API backend
• Docker containerization for easy deployment
• Real-time recommendation generation

Technologies: Python, Django, TensorFlow, Scikit-learn, Docker, PostgreSQL

Description: Enterprise-scale data engineering solutions developed at Wipro Limited, handling large-scale data pipelines and database optimization.

Key Achievements:

• Managed Oracle, MS SQL Server, DB2, and PostgreSQL databases
• Implemented backup and recovery strategies using Oracle RMAN
• Optimized performance through indexing and stored procedure tuning
• Achieved $12,000 cost savings through Elastic Pools implementation
• Developed automated ETL workflows
• Collaborated with cross-functional teams for data delivery

Technologies: Python, SQL, Oracle, MS SQL Server, Azure, ETL Tools