Remote Full-Stack Engineer (AI Skills, Ruby, React, LATAM) #26592

You´ll build critical features across our stack: order management workflows, payment processing, customer
communications, reporting dashboards, and more. As we add AI features (image-based quote estimation, chatbots,
predictive analytics), you´ll integrate ML models, design APIs for AI predictions, and ensure AI enhances the user
experience.
This is a full-stack product engineering role where AI literacy is a valuable skill. You´ll spend most of your time building
great software, and some of your time integrating AI features and working with AI/ML technologies.

Our Tech Stack
Backend
• Ruby on Rails 7.1 (primary)
• PostgreSQL with complex queries
• Redis for caching and job queues
• Sidekiq for background jobs
• Active Storage (AWS S3)
• Heroku deployment

Frontend
• React with TypeScript
• Modern responsive design
• Real-time features (Action Cable)

• Full-time Contractor
• Fully Remote — work from anywhere in the world
• Significant overlap with PST required (5+ hours) for collaboration with CTO and team
• Occasional virtual team meetups

• 3-5 years of practical full-stack engineering experience
• Strong Ruby on Rails experience (or similar MVC framework) — this is important
• Solid React/TypeScript skills — building modern, responsive UIs
• Database experience — PostgreSQL, designing schemas, writing queries
• API design and integration — RESTful APIs, webhooks, third-party integrations
• Some AI/ML exposure — integrated LLM APIs, worked with ML predictions, or basic ML concepts
• Product mindset — a product-focused approach to building solutions that are robust, scalable, and easy to use
AI/ML Background (Supplemental, Not Primary)

We´re looking for one or more of these:
• Experience using LLM APIs (OpenAI, Anthropic, etc.) and prompt engineering
• Integrated ML model predictions into web applications
• Built data pipelines for ML training or inference
• Basic understanding of ML concepts (don´t need to train models)
• Experience with AI-powered features in production
• Curiosity about AI/ML and willingness to learn


You DO need to
• Understand how to call ML APIs and use predictions in
product
• Be comfortable learning new AI/ML technologies
• Design user experiences around AI features
• Think about cost/latency trade-offs with AI

Core Product Features (75% of time)
• Order Management System: Build and enhance workflows for quote requests, order processing, status tracking,
and fulfillment
• Payment & Billing: Integrate Stripe payments, subscriptions, invoicing, and complex pricing logic
• Customer Communication: Build SMS/email systems, conversation management, and notification infrastructure
• Admin Dashboards: Create powerful analytics, reporting, and operational tools for shop owners
• Mobile-Responsive UI: Build beautiful, intuitive interfaces for non-technical users
• API Development: Design RESTful APIs for integrations, webhooks, and third-party tools
• Database Design: Work with PostgreSQL to build efficient data models and queries
AI Integration Work (25% of time)
• AI Feature Integration: Connect ML models and AI APIs to production features
• API Design for AI: Build scalable APIs that serve ML predictions efficiently
• User Experience: Design how AI features surface in the UI (confidence scores, fallbacks, explanations)
• Data Pipelines: Build infrastructure for model training data and feedback loops
• AI-Powered Workflows: Integrate computer vision quote estimation, chatbot responses, automated
categorization into product flows
• LLM Integration: Work with OpenAI/Anthropic APIs for conversational features
• Cost Optimization: Architect systems that balance AI inference costs with user experience
Your Day-to-Day
• Build features across Rails backend and React/TypeScript frontend
• Integrate AI/ML models and APIs into production systems
• Collaborate with CTO on product architecture and technical decisions
• Design database schemas, APIs, and data pipelines
• Work with LLM APIs and prompt engineering for chatbot features
• Optimize performance, costs, and user experience
• Review code and collaborate with other engineers
• Talk to customers to understand problems and validate solutions
• Own features end-to-end from design through deployment

You DON´T need to
• Train ML models from scratch
• Have a deep ML background or advanced degree
• Be an expert in PyTorch/TensorFlow
• Know the math behind neural networks