Senior Fullstack Software Engineer

Softgic · via Himalayas ·

TypeFull-time job
LocationUnited States
Posted2 hours ago
This is a remote position.

Role Overview:You will be a founding-level senior engineer in a small, fast-moving team building our core platform from scratch. This is greenfield development — no legacy code to maintain, just high-impact work that shapes the product from day one. We treat AI development tools (like Claude Code, Cursor, Copilot) as *core workflow engines*, not just helpers — you will use them daily to build features in days that would take others weeks.
You will own the full stack:- Mobile-first, rugged user interfaces designed for technicians working in busy shop environments - Backend systems that track results, flag missing steps, and automatically generate documentation, quotes, and invoices - Everything in between: data models, business logic, integrations, and intelligence layers
What You Will Build:Core Platform Engine A workflow system that guides technicians step-by-step through safety scans, system calibrations, and post-repair resets — drastically reducing errors, omissions, and compliance gaps. AI-Powered Intelligence Adaptive checklists and audit trails that adjust automatically based on vehicle make/model, repair type, and safety regulations — ensuring nothing is missed and everything is documented. Revenue & Compliance Tools Dashboards, alerts, and reports that help shop owners capture every billable service, reduce liability, and prove full compliance with industry standards. Technician-First Interfaces Simple, fast, and durable mobile/tablet UIs — built for real shop conditions: easy to read at a glance, hard to tap incorrectly, and fully usable even with dirty or gloved hands. Integrations Connect with existing tools: OEM diagnostic systems, repair management platforms, and vehicle data APIs — so our system fits seamlessly into how shops already work. 6. Advanced AI Features:- LLM-powered diagnostic summaries and natural-language documentation - Automatic conversion of repair estimates into step-by-step work orders - Intelligent automation that turns raw data into clear, actionable instructions
Requisitos

Core Skills & Requirements

We look for deep experience and strong capability in:

System Architecture & Data Modeling:Designing scalable, maintainable, and secure systems.

Security:Authentication, authorization, and data protection best practices.

Frontend:React, TypeScript, modern UI patterns, and mobile-first responsive design.

Backend:Node.js, Python, REST APIs, and service design.

Data:PostgreSQL / SQL, database design, and performance optimization.

Cloud Infrastructure:GCP, Azure, or AWS — deployment, scaling, anddevopsbasics

AI Integration:Working with LLMs, AI tools, and intelligent features.

DevOps:CI/CD pipelines, automation, and reliable delivery workflows.

Ideal Candidate Profile

You area greatfit if:

You have 5+ years offullstackdevelopment experience and can take features fromideato production alone.

You love AI coding tools — you use them daily, know how to get the best out of them, and see them asgame-changers.

You build software for real people — you care deeply about making complex things simple, especially for non-technical users.

You move fast, but never compromise on safety, accuracy, or reliability — especially important in a regulated, safety-critical industry.

You are motivated by real-world impact: your code will prevent mistakes, protect people, and help businesses succeed.

You thrive in early-stage environments: you can handle ambiguity, define scope, prototype, and iterate quickly.

You are customer-obsessed, open-minded, and always learning.

Nice-to-Have Qualifications:

Experience or familiarity with the automotive repair industry.

Knowledge of OBD-II, ADAS systems, repair workflows, or shop management software.

Experience buildingcompliant softwarein regulated fields: automotive safety, healthcare, fintech, or insurance.

Background in hardware, embedded systems, or vehicle data protocols (CAN bus, diagnostics APIs).

Hands-on work withLLM pipelines: structured outputs, retrieval-augmented generation (RAG), or AI agent workflows.

Originally posted on Himalayas
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