Remote Ruby on Rails Developer (AI Focused) USA
Role details
Job location
Tech stack
Job description
Design and implement AI-powered features within the platform (e.g., automation,
recommendations, copilots)
Integrate LLMs and/or ML models into existing services and workflows
Evaluate, select, and optimize AI tools, APIs, and frameworks for production use
Collaborate with Product to translate business problems into AI-driven solutions
Build and maintain scalable backend services to support AI functionality
Profile, test, and optimize performance of AI-integrated systems
Ensure reliability, security, and cost-efficiency of AI components in production
Contribute to architecture decisions around AI integration and system design
Partner with engineering teams to embed AI into existing applications without degrading
Requirements
3+ years of experience as a software engineer in a SaaS or cloud-based environment
Strong backend engineering experience (RoR and/or Golang preferred)
Experience integrating APIs and working within distributed systems
Hands-on experience with AI/ML tools (e.g., OpenAI, Anthropic, Hugging Face, or similar)
Experience building or integrating AI-powered features into applications (not just
experimentation)
Strong understanding of data flow, system design, and performance optimization
Experience with relational databases (SQL Server or similar)
Familiarity with microservices architecture, Kubernetes, and CI/CD pipelines
Experience deploying applications in Azure or similar cloud environments
Strong problem-solving skills with ability to work in ambiguous, fast-moving environments
Builder mindset-someone who can take an idea and turn it into a working feature quickly
Pragmatic approach to AI (focus on value, not hype)
Ability to work independently in a contract environment while collaborating closely with
internal teams
Strong communication skills and ability to explain AI concepts to non-technical
stakeholders
Preferred
Experience with prompt engineering, embeddings, or retrieval-augmented generation
(RAG)
Exposure to model evaluation, fine-tuning, or AI performance monitoring
Experience with event-driven architectures or real-time data processing
Background in energy, fintech, or other complex data-driven industries