I am a B.Tech Computer Science and Engineering student specializing in Data Science at Bennett University (2023–2027). I work at the intersection of AI product management, backend engineering, and applied AI, with an emphasis on evidence, evaluation, and clear product decisions.
- 🔭 Building AI products with deterministic validation around probabilistic models
- 🎯 Interested in AI Product Management, Product Strategy, and Product Operations roles
- 🌱 Contributing to open source across the Python and JavaScript tooling ecosystems
- 📫 Open to product and AI engineering opportunities
Languages
AI & Backend
Frontend
Data & Infrastructure
| Project | What it demonstrates | Evidence |
|---|---|---|
| BenNest (private code) | A zero-brokerage marketplace for student housing, flatmates, and vehicle listings, with role-specific workflows, moderation, enquiries, messaging, and privacy-conscious authentication | Next.js · TypeScript · Supabase · email OTP · signed server sessions · live product |
| ProdIntel AI | Converts stakeholder feedback into evidence-traceable product decisions using a four-stage AI pipeline, deterministic validation, RAG, and decision provenance | Live demo · 569 tests · 12 architecture decisions |
| Tabyss | A privacy-first browser extension that computes your browsing personality entirely on-device — zero network requests, zero tracking | Edge Add-ons · source code · JavaScript · on-device analytics |
| AWS Remote Device Control (CDC) | A standalone Windows desktop executable for controlling and diagnosing remote Android devices through secure EC2/SSH tunnels | Python · PySide6 · AWS EC2 · ADB/scrcpy · automated tests · Windows .exe distribution |
| Multi-Agent Research Pipeline | Turns a research question into a structured, cited report using LangGraph agents, FastAPI, Claude, Tavily, arXiv, Qdrant, and deterministic offline mocks | CI · Docker · tests · evaluation fixtures · zero-key demo mode |
User and stakeholder signals
↓
Clear product requirements
↓
Typed, testable system design
↓
Deterministic validation and evaluation
↓
Explainable product outcomes
I care about the boundary between what an AI model produces and what a product can safely trust. My projects emphasize traceability, explicit trade-offs, realistic product scope, and documentation that makes technical decisions reviewable.