AI Fullstack Developer

• 4+ years of commercial experience with JavaScript/TypeScript  
• 3+ years of commercial experience with Node.js on the back end  
• Experience building and maintaining REST/JSON APIs  
• 2+ years of commercial experience with a modern front-end framework Vue.js or React.js 
• Experience building SPA dashboards, forms, data grids, and filters  
• Experience working as a true full-stack developer (same engineer owning both backend and frontend features end-to-end)  
• Experience integrating with external APIs and third-party services  
• Experience with unit/integration testing for back-end and/or front-end  
• Experience with AI / LLM integrations (OpenAI, Claude, etc.) from Node.js   
• Experience with AI-assisted development (like Claude Code or Cursor) 
• English: Intermediate+ (able to participate in daily stand-ups, grooming sessions, and client calls)  

Nice to have:  

• Knowledge of Docker and container-based deployment  
• Familiarity with cloud platforms (AWS / Azure) and managed databases  
• Experience with CI/CD pipelines (GitHub Actions, Azure DevOps, GitLab CI, or similar)  
• Knowledge of real estate / financial / valuation domains  
• Experience building Retrieval-Augmented Generation (RAG) pipelines leveraging vector databases 

• Work as a full-stack engineer, owning features from API to UI:
• Develop and maintain Node.js backend services for data ingestion, processing, and application logic
• Collaborate with solution architects and data engineers to:
• Align on data models and contracts between the application and the data warehouse
• Integrate with:
• External data providers and internal APIs
• Ensure code quality:
• Write clean, testable, maintainable code in both the back end and the front end
• Participate in code reviews, refactoring, and technical design discussions
• Contribute to architecture and technical decisions for the MVP and further phases
• Collaborate with PM/BA and client stakeholders, clarify requirements, and propose implementation options
• Help improve DevOps and delivery practices (logging, monitoring, basic CI/CD improvements) within the scope of your work
• Continuously learn and adopt best practices around data-intensive systems and AI-assisted development