Full-Stack & AI Software Engineer
M.Tech Computer Science & Engineering candidate and B.Tech Information Technology graduate building accessible web products with React, TypeScript, FastAPI, Python, and practical AI.
I enjoy taking software from an ambiguous problem to a tested, documented deployment: designing the interface, validating the API, modeling the data, handling failure states, and automating the release checks.
| Project | What it demonstrates | Links |
|---|---|---|
| QualiTrace | Pharmaceutical complaint intake, human review, CAPA and audit workflows using React, FastAPI, PostgreSQL, and LangGraph | Live demo · Repository |
| Grounded Ops RAG | Citation-aware retrieval, corpus management, evaluation, bias probes, and cost reporting with a TypeScript operations console | Live demo · Repository |
| AI Research Assistant | Multi-user PDF processing, hybrid search, grounded Q&A, comparison, classification, and analytics | Live demo · Repository |
| Portfolio | Responsive, keyboard-accessible static UI with automated markup, link, build, and Lighthouse checks | Live site · Repository |
Live application links were verified on 26 July 2026. Render services on a free plan may need a short cold-start period.
- Menubar (Electron/TypeScript): designed and submitted an idempotent teardown API that releases owned windows, trays, timers, and scoped event listeners while preserving caller-owned resources. Issue #281 · PR #504 — under review
- Frontend: React, TypeScript, JavaScript, Vite, Redux Toolkit, semantic HTML, responsive CSS, accessibility
- Backend: Python, FastAPI, Flask, Node.js, Express, REST APIs, authentication, validation, background processing
- Data & AI: PostgreSQL, SQLite, SQLAlchemy, Pandas, scikit-learn, TensorFlow, RAG, hybrid retrieval, LangGraph
- Quality & delivery: Pytest, Vitest, Ruff, dependency auditing, Docker, GitHub Actions, Render, GitHub Pages
- Make loading, empty, error, and recovery states part of the feature.
- Prefer strict validation, least-privilege configuration, and safe public demos.
- Keep AI output grounded, cited, reviewable, and clearly separated from deterministic behavior.
- Add tests and deployment documentation that another engineer can actually run.
- Keep claims tied to working code and verified deployments.
I am strengthening production backend design, TypeScript application architecture, AI evaluation, observability, and cloud deployment while building end-to-end projects.
I’m open to full-stack, React/frontend, backend, Python/FastAPI, MERN, AI-enabled software engineering, internship, and entry-level opportunities.
Recruiters and engineering teams: the quickest way to evaluate my work is to open one of the live projects above, review its repository documentation, and contact me through LinkedIn or email.