Senior Ruby on Rails Engineer, AI
RemoteEcuador
Job Summary
Build scalable product features using Ruby on Rails and React while integrating LLM-powered workflows and debugging large production applications. Design and ship retrieval-based systems, structured LLM integrations, and AI agents that automate business workflows. Evaluate AI systems through testing frameworks and implement Retrieval-Augmented Generation architectures with vector databases. Collaborate through code reviews and Agile practices to adopt modern AI development practices across international clients. Based in Latin America, this role combines backend engineering with practical AI solution design. ioet provides Software Engineering as a service to startups and global brands, offering structured career plans and mentorship.
Required Qualifications
- 5+ years of professional experience developing applications with Ruby on Rails
- Experience working in large, production-grade Rails applications or monolithic architectures
- Hands-on experience designing and shipping LLM-powered features in production
- Experience building retrieval-based systems, AI workflows, or structured LLM integrations
- Experience evaluating AI systems and measuring model quality through testing or evaluation frameworks
- Strong understanding of Ruby, JavaScript, and modern software engineering best practices
- Working knowledge of React to develop, test, and troubleshoot frontend features
- Experience integrating with third-party APIs and external services
- Strong debugging, problem-solving, and architectural decision-making skills
- Experience working collaboratively through code reviews, technical discussions, and Agile development practices
- Strong English communication skills – Minimum B2 level proficiency
- Send your application and CV in English (mandatory)
- Based in Latin America
Desired Qualifications
- Experience designing AI agents capable of interacting with business systems or automating workflows
- Experience implementing Retrieval-Augmented Generation (RAG) architectures
- Familiarity with vector databases, semantic search, or embedding-based retrieval systems
- Experience building AI evaluation pipelines and observability for LLM applications
- Experience with Heroku, Vercel, or similar cloud deployment platforms
- Experience mentoring engineers and promoting AI best practices across development teams
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