Intermediate Ruby on Rails Developer

SUMMARY:
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

• 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

POSITION INFO:
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