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Fullstack Engineer with 5+ years in React, Python, and AI/ML
Front-end Developer with 5+ years of experience building scalable web and mobile applications across Healthcare and AI domains, now actively transitioning into full-stack development. Brings deep expertise in React.js, TypeScript, and GraphQL, complemented by hands-on back-end experience with FastAPI and Python through personal projects - including an end-to-end AI-powered document Q&A platform. Eager to contribute across the full stack while leveraging a strong foundation in performance optimization, accessibility, and remote Agile collaboration. Architected and shipped AI Doc QA, a full-stack document intelligence platform using React + TypeScript, FastAPI, and Sentence Transformer AI models for semantic search and Q&A over uploaded PDFs. Designed and implemented reusable component libraries adopted by 15+ engineers, improving development speed by ~30% and code consistency. Strong performance optimization background - achieved up to 40% faster load times through code-splitting, lazy loading, and optimized API query strategies. Experienced in both front-end (React, GraphQL, Tailwind CSS) and back-end (FastAPI, Django, Temporal, REST APIs) with hands-on work in HIPAA-compliant environments. Expert in modern testing practices (Jest, React Testing Library, Cypress) with 85%+ test coverage and automated E2E test suites.
Shree Swami Atmanand Institute of Technology
B.Tech. / B.E. · Computer Science & Engineering
N/A – June 30, 2019
Frontera
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CogniAble
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Web Design Intern
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November 1, 2020 – February 1, 2021
India
AI Doc QA - Full-Stack AI Document Intelligence Platform
June 15, 2026 – Present
Designed and built a full-stack AI-powered document Q&A platform enabling users to upload PDFs and query them using natural language. Front-end: React + TypeScript SPA with drag-and-drop PDF upload, real-time streaming Q&A interface, and citation highlighting built with Tailwind CSS. Back-end: FastAPI (Python) REST API handling document ingestion, chunking, and serving semantic search results; clean async architecture with background processing. AI Layer: Integrated Sentence Transformer models (all-MiniLM-L6-v2) to generate semantic embeddings; FAISS vector store for fast similarity search and context retrieval. Pipeline: PDF text extraction → chunking → embedding → FAISS indexing → retrieval-augmented generation (RAG) for accurate, grounded answers. End-to-end ownership: database schema, API design, AI model integration, UI/UX, and deployment configuration.
Cultural Fit Analysis
The candidate exhibits a strong cultural fit for a dynamic, tech-forward environment. Their personal project involving AI and full-stack development showcases initiative, continuous learning, and a passion for cutting-edge technologies. Experience in both front-end and full-stack roles, coupled with a diverse skill set (React, Python, FastAPI, GraphQL, AI/ML), indicates adaptability and a willingness to tackle varied challenges. The focus on collaboration, code quality, and performance aligns well with best practices in modern software development teams.
Soft Skills & Operational Fit
The candidate demonstrates strong operational fit through experience in remote Agile teams, CI/CD pipeline contributions, and a focus on code quality and testing. Their ability to drive projects end-to-end, from design to deployment, indicates strong ownership and problem-solving skills. The emphasis on reusable components and performance optimization suggests a proactive approach to maintainability and scalability. Experience in healthcare domains also points to an understanding of regulated environments.