Mounika Pindi
Career Objective
Entry-level Software Engineer with 0 years of experience building full-stack web applications and AI-driven assistants during academic projects and internships. Strong practical experience developing Flask backends and React frontends, integrating MySQL databases, and delivering RESTful APIs. Built a production-style food-donation platform with role-based authentication, responsive React UIs, and backend data models. Implemented RAG pipelines, semantic search with FAISS, OCR ingestion using Tesseract, and LLaMA-3 inference via Groq API for a legal-assistant project. Comfortable applying data structures & algorithms, OOP, and DBMS fundamentals to optimize indexing and query performance. Proficient with Git/GitHub and modern developer workflows; eager to contribute to entry-level backend, full-stack, or ML-integration roles and continue learning on production codebases.
Education
Technical Skills
Projects
- Built RESTful backend APIs using Flask to expose donation and user-management endpoints and to handle CRUD operations for donations.
- Implemented responsive frontend interfaces with React.js and Bootstrap to support donors, collectors, and admin dashboards.
- Designed normalized MySQL schemas and authored optimized SQL queries to manage donors, donations, and inventory records.
- Implemented role-based authentication and distinct dashboards to separate donor, collector, and admin workflows for secure access control.
- Integrated image upload and verification flows, storing image metadata in the database and ensuring traceability for donation records.
- Developed a retrieval-augmented generation (RAG) pipeline in Python that indexes legal documents and serves semantically relevant context for queries.
- Implemented semantic vector search using FAISS to enable low-latency retrieval of relevant legal passages for downstream generation.
- Built FastAPI endpoints to accept uploads, perform OCR ingestion, and return synthesized answers from indexed documents.
- Integrated Tesseract OCR to extract text from scanned legal documents and preprocessed text for vectorization and indexing.
- Implemented LLaMA-3 inference through the Groq API to generate multilingual, context-aware responses from retrieved document context.
Certifications
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