Senior Fullstack Software Engineer

Este es un puesto de trabajo remoto.

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, and devops basics

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

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

Ideal Candidate Profile

You are a great fit if:

• You have 5+ years of fullstack development experience and can take features from idea to production alone.

• You love AI coding tools — you use them daily, know how to get the best out of them, and see them as game-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 building compliant software in regulated fields: automotive safety, healthcare, fintech, or insurance.

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

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