Senior Frontend Engineer

Gurjit Chahal

I build React and Next.js applications with strong frontend architecture, workflow-driven UX, and measurable product outcomes.

8+ years building customer-facing and internal tools, including support workflows used in retail operations at Walmart. I focus on reusable component systems, accessibility, and performance in complex interfaces.

View Projects Resume Highlights gurjitchahal96@gmail.com LinkedIn

Technical Expertise

Frontend

React
TypeScript
JavaScript
Next.js
Angular

Backend & Data

Node.js
Express
REST APIs
GraphQL
MySQL
MongoDB

Cloud & Tools

Kubernetes (GKE/WCNP)
Docker
GCP
Azure
Jest
JMeter
Splunk

Personal Project Case Studies

Independent builds where I owned product direction, architecture, and implementation end-to-end.

Personal Project

Scheduling System

Problem: Teams needed a scheduling UI that handled conflicts, date navigation, and fast task completion.

Ownership: Owned interface design and implementation for week/month/year views, form validation, and booking flows.

Challenge: Balanced quick data entry with conflict prevention and clear visual hierarchy across different calendar densities.

Outcome: Delivered a reusable scheduling shell pattern that can support additional booking workflows with minimal UI rework.

Stack: Next.js, React, TypeScript

Open Demo Read Case Study Source

Personal Project

AI Scheduler Assistant

Problem: Users wanted to schedule from natural language without learning calendar-specific input formats.

Ownership: Built the NLP-to-event interaction model, timezone-aware rendering, and guided conflict fallback UX.

Challenge: Turned ambiguous language into deterministic UI actions while keeping edits explainable to the user.

Outcome: Reduced scheduling friction by translating free-text intent into structured events with conflict recovery paths.

Stack: Next.js, React, TypeScript, OpenAI patterns

Open Demo Read Case Study Source

Personal Project

CRM Workflow Platform

Problem: Support agents needed a guided workflow to handle order, membership, and returns without losing context.

Ownership: Designed reusable step-based UI patterns, chat panel interactions, and action shortcuts for high-velocity tasks.

Challenge: Modeled workflow state transitions clearly while preserving flexibility for exceptions and escalation paths.

Outcome: Improved flow clarity with a predictable step model that makes complex support interactions easier to navigate.

Stack: React, TypeScript, Component architecture

Open Demo Read Case Study Source

Personal Project

Developer Analytics Dashboard

Problem: Engineers needed an at-a-glance view of service demand, error behavior, and latency trends.

Ownership: Built responsive dashboard cards, visualized trends, and added compact insights for quick triage decisions.

Challenge: Generated realistic synthetic data so metric relationships remain plausible when exploring time ranges.

Outcome: Created a trustworthy demo surface that communicates product thinking and frontend data-visualization craft.

Stack: React, TypeScript, Data visualization

Open Demo Read Case Study Source

Professional Experience

Professional Case Study

AI Agent Assistant (Walmart)

Built a generative AI-powered assistant integrated into Walmart's customer care platform to support agents during live customer interactions.

Note: Details are shared at a system-design and implementation level to respect internal confidentiality.

Problem

Customer service agents had to manually search across multiple internal tools while handling live chats or calls, increasing handle time and cognitive load.

The challenge was integrating AI into a real-time workflow without disrupting existing systems or introducing latency.

Approach

Streamed live conversation context to backend services.
Processed messages through an AI pipeline.
Returned contextual suggestions in near real-time.

Architecture Decisions

Used Kafka for event-driven message flow between services.
Combined polling for UI updates with backend streaming to balance reliability and complexity.
Kept AI orchestration decoupled from frontend rendering.

Tradeoff: WebSockets were considered but deferred due to infrastructure constraints and operational overhead; polling gave predictable behavior under load.

Implementation

Frontend: React, Next.js, TypeScript. Backend integration: event-driven services via Kafka.

Built real-time suggestions UI, contextual prompts, and agent-side interaction panel.
Implemented retry and fallback handling for failed AI responses.

Challenges and Decisions

Balanced latency versus response quality from AI systems.
Coordinated asynchronous updates across multiple backend services.
Added guardrails for non-deterministic AI output.

Key decision: Introduced graceful degradation so core support workflows continue even when AI responses fail.

Impact and Next Iteration

Reduced average customer handle time (~30%).
Improved agent efficiency and response consistency.
Launched from MVP to production across support teams.
Next: add WebSockets or SSE as the platform matures.
Next: introduce smarter caching for repeated query patterns.
Next: improve observability around AI response quality.

Engineering Focus

Reusable Component Systems

Designing composable UI primitives and step-based flows that can be reused across product surfaces without losing clarity.

Performance + Accessibility

Optimizing rendering paths, loading states, and semantic structure so interfaces remain fast and usable under real-world constraints.

Product Integration

Translating API behavior into understandable UI states, guardrails, and error handling that help users complete workflows confidently.

Selected Experience

Walmart retail support workflows and internal tooling

Core Domain

Workflow-heavy interfaces for operations and customer support

Contact

gurjitchahal96@gmail.com

Hiring for frontend ownership?

I am targeting senior frontend roles focused on React, Next.js, and workflow-driven product experiences.

Contact Me