Bangalore, India
Software Engineer at Kantar, building analytics platforms end to end — modular React frontends, GenAI experiences, and event-driven backends on AWS.
I build reliable, scalable software from frontend to backend.
Build polished, accessible interfaces and reusable component systems that are easy to maintain and scale.
Take a product from idea to production, making the right technical decisions along the way without overengineering it.
Design the structure of a product across frontend, backend, data, and infrastructure, with a focus on keeping systems simple, reliable, and easy to evolve.
Find and fix bottlenecks that make applications slow — from rendering and data fetching to architecture and infrastructure.
Design APIs, data models, and backend services that support reliable products and can evolve as the product grows.
Build practical AI features that integrate naturally into existing products — from AI interfaces and workflows to tools and agent integrations.
Kantar · Bangalore, India
Built a reusable component system on Material-UI with custom shared components, integrated Storybook for development and documentation, and aligned implementations with the product design system for consistency, reusability, and scalability across analytics workflows.
An interactive dashboard letting users evaluate "what-if" marketing scenarios and predict KPI impact through charts, tables, and cards.
Built an AI analytics chat assistant end to end — conversational state, persisted paginated history, API integration, Redux workflows, and a typed block-renderer architecture mapping AI response blocks to reusable Markdown and D3 visualization components.
Improved a hierarchical table handling ~10,000 rows: converted recursive rendering into a flat visible-row structure with virtualization, moved SWR fetching from rows to the parent, and optimized React reconciliation — ~9 minutes → 54 seconds.
Raised codebase coverage from ~30% to 95%, introducing stronger type safety across API responses, Redux state, and reusable React components while improving maintainability.
Architected backend services in Node.js and Go on AWS (S3, SQS, Lambda, DynamoDB, PostgreSQL), defining APIs, metadata models, and event-driven workflows for onboarding, activation, output retrieval, and model lifecycle management.
Designed lifecycle and metadata APIs across 4 model flows — portfolio, long-term, combined, and brand-level analytics — with backward-compatible metadata and migration support for new admin, prediction, and overview experiences.
Defined and implemented RBAC for an analytics admin surface, expanding a 2-role model to 7 role profiles with multi-organization access — launched with zero post-release bugs or user queries.
Built an MCP server over existing platform APIs, turning backend capabilities into discoverable, reusable tools for AI clients so users can interact with analytics through an agent.
Interactive platform for learning browser and DOM internals
EventTarget, Node, Element, HTMLElement, Document, and Window.B.Tech in Civil Engineering · CGPA 8.61
Open to interesting problems in frontend architecture, performance, and applied AI.