Building and scaling student-facing platforms at India's largest EdTech unicorn — from Career Wallah and Tracker 360 (350K+ combined active users) to the Khazana migration and automation pipelines ingesting lakhs of hours of content.
Ideated and pitched Career Wallah, a personalized college-prediction and career-guidance platform. I currently own its end-to-end development — architecture, backend, frontend and shipping. It now serves 150,000+ active users with 200,000+ sign-ins.
Designed and independently developed Tracker 360, a personalized study-planning and progress-tracking platform that dynamically generates student-specific learning plans and tracks progress against them. Now serves 200,000+ active users.
Led the Khazana 1.0 → 2.0 migration: designed the system architecture and database schema, built the migration pipelines, and coordinated a multi-team rollout to move 300,000+ records onto the new schema without downtime for students.
Owned and architected the automation layer for Khazana, enabling end-to-end autonomous ingestion of 200,000+ hours of video and 1M+ notes — supporting a platform used by 2Cr+ students with zero manual intervention in the content pipeline.
Built and scaled score calculators, rank predictors and counseling tools that hold up under exam-day traffic spikes, serving 500,000+ users. Developed Node.js/Python data pipelines that extract, clean and process 170,000+ student result records across JEE, CBSE, ICSE and state boards.
Shipped internal tools in as little as a day, including a bulk certificate generator (10,000+ certificates generated), a Next.js automation portal that cut a recurring Khazana workflow from 48 hours to 2, and a faculty resource-planning platform.
Architecture notes on the three systems I own. Two of them have full case studies.
A single Next.js 16 service on Kubernetes. Prediction engines run over 30,882 cutoff rows held as committed JSON in process memory, so no database read sits on the hot path. MongoDB spans four logical databases for leads, payments and OMR submissions; Redis holds PW tokens, entitlement flags, rate limits and live per-booklet aggregates.
A pure, unit-tested scheduling engine derives each day's list, backlog and pace from a small plan document on every read. Course content ships as version-pinned JSON; MongoDB stores plans and members with a unique partial index guaranteeing one active plan per student; Redis holds PW tokens and rate-limit counters.
A self-service portal where category teams file content sheets, a superadmin approves them, and an in-process worker publishes them to PW's admin APIs across three hosts and API versions — with no message broker. MongoDB is both the store and the queue.
Products I shipped that are live today, with real usage numbers.