Backend Engineer · AI/ML Developer · Charlotte, NC
I'm Bindu Vasini Potipireddi — a Software Engineer who builds at the intersection of backend systems and artificial intelligence. On the engineering side, I design distributed, event-driven microservices using Java, Spring Boot, and Apache Kafka. On the AI side, I develop ML pipelines, work with Generative AI, and build LLM-integrated applications using Python and TensorFlow. With 4+ years of experience and certifications in Python, Generative AI, and Deep Learning, I bring both the technical depth to architect systems and the AI fluency to make them intelligent.
Built Spring Boot REST services integrated with Kafka event streams for scalable financial data processing Developed Kafka producers and consumers with retry logic and fault-tolerant event pipelines Optimized SQL validation and reconciliation workflows across large datasets improving data accuracy Prototyped LLM-assisted automation using Python and OpenAI GPT APIs to generate structured outputs
Built Python automation scripts converting Excel datasets into Kafka JSON streams Processed 1000+ records per run improving ingestion efficiency and reducing manual work Integrated scripts into CI/CD pipelines enabling consistent execution across environments
Assisted students with SQL optimization, database design, and query performance Conducted labs helping students debug queries and understand database concepts Created documentation guiding students through practical database problem solving
Assisted in configuring and maintaining university servers and workstations. Supported deployment and troubleshooting of university software systems. Managed and resolved 20+ weekly helpdesk tickets across departments. Trained new student workers on IT support procedures and tools.
Built a RAG application using Python, LangChain, and OpenAI GPT to answer research questions from document datasets Implemented vector embeddings and semantic search for contextual retrieval before generating responses Developed a FastAPI backend and applied prompt engineering to improve response accuracy and reduce hallucinations
Technologies: Python, LangChain, OpenAI GPT, FastAPI
Implemented Kafka real-time event pipelines with SQL validation for reliable event-driven ingestion Built workflows for message transformation, schema validation, and downstream service integration Added retry logic and testing to improve pipeline reliability and data consistency
Technologies: Spring Boot, Kafka
Developed Spring Boot and Kafka microservices for tax slip generation improving reporting accuracy by 25% Implemented REST APIs and asynchronous messaging following microservices architecture
Technologies: Spring Boot, Kafka, REST APIs
Built a TensorFlow deep learning model in Python to classify facial aging features Applied data preprocessing, feature extraction, and supervised learning with model evaluation
Technologies: Python, TensorFlow
Built an end-to-end data intelligence system analyzing IRS ZIP-level data to identify high-value luxury markets Engineered features like passive income ratio and stability score to model luxury consumer behavior Developed a Random Forest ML model with feature engineering and validation to predict high-potential regions Designed a hybrid decision system combining rule-based scoring and ML predictions
Technologies: Python, ML, Streamlit, LLM