Intermediate Ruby on Rails Developer

Reference: JNB008110-MNT-1

What you'll do

• You'll spend your time building software that delivers real value to customers while using AI to accelerate development and improve engineering quality

On any given week you might

• Build new product features across our Ruby on Rails applications

• Collaborate with Product and Design to rapidly prototype and iterate on ideas

• Design and build REST and GraphQL APIs powering our web and mobile platforms

• Use AI coding assistants to accelerate implementation while maintaining high engineering standards

• Write clean, maintainable and well-tested code

• Improve existing functionality through refactoring, optimisation and continuous iteration

• Review AI-generated code critically, validating correctness, performance and security

• Contribute to sprint planning, technical design discussions and architectural decisions

• Work with PostgreSQL, Redis and background jobs to build reliable, scalable systems

• Improve our CI/CD pipelines, engineering tooling and developer experience

• Explore opportunities to incorporate AI capabilities into our products where they create genuine customer value

Duties & Responsibilities

What we're looking for: You'll probably have:

• 3–5 years of professional software development experience

• Strong commercial experience with Ruby on Rails

• Excellent understanding of Ruby and Rails conventions

• Experience designing and consuming RESTful APIs

• Strong SQL skills and experience working with PostgreSQL

• Experience using Sidekiq or similar background processing frameworks

• Confidence writing automated tests

• Experience using Git and modern software development workflows

• Strong problem-solving and debugging skills

• Excellent communication and collaboration skills

AI Proficiency (Essential)

• Using AI coding assistants such as GitHub Copilot, Claude, ChatGPT, or Cursor

• Using AI to accelerate feature development while maintaining high engineering quality

• Writing effective prompts that produce reliable engineering outputs

• Critically reviewing, validating, and improving AI-generated code rather than accepting it blindly

• Using AI for debugging, refactoring, testing, documentation and technical research

• Understanding the strengths and limitations of large language models

• Continuously experimenting with new AI tools to improve developer productivity

Our stack: Backend:

• Ruby on Rails

• PostgreSQL

• Redis

• Sidekiq

• REST and GraphQL APIs

• OpenTelemetry

• Sentry

• New Relic

• Datadog

Frontend

• Hotwire (Turbo and Stimulus)

• JavaScript

• TypeScript

Infrastructure

• AWS

• Docker

• GitHub Actions

• CircleCI

AI Developer Tooling

• GitHub Copilot

• Cursor

• Claude

• ChatGPT

• MCP-compatible development tools

• AI-powered code review and debugging tools

• LLM APIs (OpenAI, Anthropic, Google Gemini or similar)

Bonus points

• AI-powered application development

• LLM APIs (OpenAI, Anthropic, Gemini or similar)

• AI agents and workflow automation

• MCP (Model Context Protocol)

• Prompt engineering

• AI evaluation and testing techniques

• High-volume transactional applications

• Payment platforms

• GraphQL

• Performance optimisation and SQL tuning