Hi, I'm Reetam 👋

Responsibilities

• Owned core product flows in a production Next.js app, spanning payments, authentication and system performance.

• Led SEO-driven shift from client-side rendering to server components with ISR, cutting LCP from ~5.2s to ~2.3s and TBT from ~280ms to ~170ms, enabling indexing of 5M+ dynamic pages.

• Implemented Razorpay payments end-to-end: one-time payments, subscriptions, secure order verification APIs, and webhook pipelines for backend sync.

• Led migration of authentication system to a major SDK version upgrade (Auth0 v3 to v4) with zero production downtime, reworking middleware, APIs, and client flows to align with new SDK architecture.

• Cut redundant network requests by ~60% and boosted initial load and data fetch performance by ~2–3× through frontend and API refactoring and caching.

• Implemented cross-platform iOS, Android, and Web support using Capacitor, integrating native push notifications, filesystem access, and RevenueCat-powered in-app purchases.

Responsibilities

• Built a high-throughput, fault-tolerant event ingestion system in Go, streaming data through Kafka into durable S3-compatible object storage through a sink connector, fully containerized with Docker.

• Rebuilt the ingestion pipeline to replace a monolith (~2,000 events/sec) with a microservices architecture using Golang sustaining ~28,000 events/sec - a ~14x throughput gain.

• Achieved 99.99% message persistence across ~5M events under sustained load.

• Ensured fault tolerance via a bounded queue with HTTP backpressure, write retries and dead-letter queues for failed messages, preventing data loss and silent failures under traffic spikes.

Responsibilities

• Built a Next.js/React recruitment platform with role-based access for companies, job seekers, and admins.

• Developed 5+ major features including landing page, job flow, dashboards, and detail views.

• Migrated legacy PHP features to React, improving UX and maintainability.

• Created Recharts visualizations for recruitment metrics, enabling data-driven insights.

Responsibilities

• Optimized the Bonsai ML model for IoT devices with strict 2KB RAM constraints.

• Achieved 95%+ accuracy through hyperparameter tuning and projection refinement.

• Analyzed trade-offs between memory, model size, and decision tree performance.