Sathwika Bandaru
AI Engineer building LLM, RAG & AI automation solutions
New to Contra
Sathwika is ready for their next project!
Collaborative project, contributed key production components of Med-V²QA, a clinical Visual Question Answering system built on the MUMC architecture for radiology image analysis.
My contributions: built the Dual-Gate Guardrail System (Gate 1 rejects non-medical intent, Gate 2 uses a local CLIP classifier to reject non-medical images ensuring clinical safety and system focus); developed the Batch Triage module using zero-shot CLIP to automatically sort and prioritise the most abnormal scans from uploads of up to 20 images; integrated Voice I/O using OpenAI Whisper for speech-to-text transcription and gTTS for audio readout of answers; built the PDF report generation module producing structured clinical reports with patient data, notes, and VQA results; and developed the FastAPI backend handling high-performance async request processing across all services.
Built as part of a team my work covered the safety layer, multimodal I/O, clinical reporting, and backend API infrastructure.
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Collaborative project built at Kaggle Week 26 GenAI Hackathon, contributed the core AI engineering layers of a Business Law RAG Assistant that answers legal document queries with source citations and live faithfulness verification.
My contributions: built the hybrid FAISS + BM25 retrieval pipeline for accurate document search; designed the multi-agent orchestration system coordinating retrieval, generation, and verification agents; implemented the faithfulness verification layer to evaluate and score LLM output quality; integrated an MCP tool server enabling live web search as a fallback; and built the analytics dashboard tracking query performance and retrieval metrics.
Built as part of a 4-person team, my work covered the RAG pipeline, agentic architecture, and evaluation infrastructure.
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Built as part of a team, my contributions covered the pipeline orchestration, model serving, and data infrastructure layers.
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Built a Medical NLP system that processes clinical text to extract healthcare entities, redact sensitive information, and structure unstructured medical data using NER pipelines and FastAPI for backend integration.
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