Revathi S A

Project: Agentic AI Clinical Letter & Summary Generation System
• Designed and deployed NLP and LLM pipelines for automated clinical document generation using Python, FastAPI, and Azure OpenAI.
• Developed production APIs, workflow orchestration, model validation, and multi-agent AI pipelines with automated quality assurance.
• Containerized applications using Docker and implemented CI/CD using Azure DevOps.
Impact: Enabled efficient, high-quality automated clinical documentation, reducing manual effort and improving consistency, compliance, and scalability in medical data processing workflows.

Project: Product Data Reconciliation System
Designed and deployed large-scale batch and streaming data pipelines processing millions of product records using Dataflow, Kafka, BigQuery, and Kubernetes.

• Designed large-scale batch and streaming ML pipelines processing millions of retail product records using Dataflow, Kafka, BigQuery, and Kubernetes.
• Developed feature engineering, anomaly detection, predictive ML models, and data validation workflows to improve product quality.
• Implemented production monitoring, observability, model validation, and performance evaluation for enterprise ML systems.
• Collaborated with cross-functional teams to deliver scalable ML solutions, improving product data accuracy by 15%.

Impact: Improved product data accuracy by 15% and strengthened observability and reliability of large-scale data pipelines.

Project: GPT-4 Q&A Financial Bot
Duration: Nov 2023 – Feb 2024
• Built a RAG-based financial document Q&A system using GPT-4, Pinecone, semantic search, and metadata filtering.
• Implemented evaluation pipelines, explainability, prompt optimization, and scalable REST APIs.

Impact: Delivered high-precision, context-aware answers and established a governance-ready foundation for enterprise AI adoption.

Project: PDF ETL & Elasticsearch Integration
Duration: Oct 2023 (3rd week) – Nov 2023 (3rd week)
• Built PDF parsing and Elasticsearch indexing pipelines using Python.
• Reduced manual ETL effort by 80% through automated extraction and optimized search.
Impact: Reduced manual ETL effort by 80% and significantly improved ingestion-to-search turnaround time.

Project: NASDAQ Stock Visualization Using Chart.js
Duration: Oct 2023 (1st–2nd week)
• Developed LLM-driven Chart.js visualizations with automated schema detection.
Impact: Enabled faster self-service data visualization with minimal manual setup for end users.

• Performed EDA, feature engineering, clustering, and predictive modeling on large e-commerce datasets using Python, SQL, Pandas, and Scikit-learn.
• Built customer segmentation and healthcare prediction models using KNN, RFM analysis, cross-validation, and hyperparameter tuning.

• Developed CNN and DenseNet-based deep learning models for medical image classification and segmentation.
• Applied transfer learning, NLP, and LLM techniques for document understanding and AI-assisted healthcare applications.
• Published four peer-reviewed papers in Deep Learning and Medical AI.

• Developed and delivered AIML and Data Science curricula, mentoring students in Machine Learning, Responsible AI, and Explainable AI. • Conducted research workshops and published work in Deep Learning and Medical AI.

• Developed monitoring services for GPU and CPU using C++.