Backend Engineer (Node.js / Redis) - Strictly In-Office, Indore
The Hustle House
Location
🇮🇳 Indore, India
Type
full_time
Salary
Undisclosed
Posted
19h ago
Job Description
Backend Engineer (Node.js / Redis) — Strictly In-Office, Indore Company: The Hustle House Location: Indore, MP (100% In-Office) Compensation: ₹25,000 – ₹40,000 / month (Based on technical evaluation)
Experience Required
: 1+ years of production-grade backend development --- READ THIS FIRST: THE HARD FILTERS Before you read further, understand that we run a tight, high-output engineering team. We do not have time for generic, one-click applications. • 100% In-Office: This role is based entirely at our Indore office. There is no remote option, no hybrid option, and we are not waiting for out-of-station candidates to relocate. • In-Person Interviews Only: All technical assessments and interviews happen live at our office. Requests for Zoom/Google Meet calls will result in immediate disqualification. • Application Gate: If you apply without completing the application instructions at the bottom of this page, your resume will be deleted unread. ---
THE ROLE
One Seat. Filling This Month. Our current project is a WhatsApp-native agentic middleware platform live in production with active daily enterprise users. We are not building a prototype or a pitch deck; we are scaling a live system. You will work directly under our CTO, building the Node.js systems that power the platform: the API layer, queue-based delivery infrastructure, and heavy database integrations. Your first project will ship directly to a paying client environment within month one. In exchange for this high-growth environment and direct mentorship, we keep our budget locked to local Indore market standards (₹25k–40k/mo). If you are looking for corporate-level corporate salaries, this isn't it. If you want unmatched production ownership, read on. ---
WHAT YOU WILL DO
- Build, optimize, and maintain high-throughput backend features using Node.js and Express.
- Manage data architecture and optimization across MongoDB and Redis.