AI Fullstack Developer

This position is ideal for a senior-level full stack engineer with 5+ years of experience who is equally strong in frontend development with React and backend development with Node.js, and who is comfortable owning end-to-end web applications. It will suit someone who has a solid grasp of RESTful API design, MVC architecture, and scalable system design, and who enjoys writing clean, maintainable, and well-tested code. The role is especially attractive for developers who have hands-on experience integrating AI or ML capabilities into production systems—either through direct work with Python-based models or via third-party AI APIs—and who are excited about building intelligent, data-driven features. It fits engineers who are proactive, performance-minded, and confident working in cloud environments with modern CI/CD pipelines, while collaborating closely with cross-functional teams in an agile, fast-moving setting.

1. What is your experience building full stack applications with React and Node.js/Express?
   I’ve built and maintained several production full stack apps using React + TypeScript on the frontend and Node.js/Express on the backend, handling everything from authentication and routing to data fetching, error handling, and deployment in cloud environments.

2. How do you structure a Node.js/Express backend using MVC architecture?
   I separate concerns into models (DB schemas and data access), controllers (request handling and business logic orchestration), and views or API responses, with services/utilities extracted when logic grows, keeping routes thin and controllers focused.

3. Can you describe how you design and document RESTful APIs?
   I start from the domain and use resource-based endpoints, clear HTTP verbs and status codes, consistent naming, and pagination/filtering where needed, then document with OpenAPI/Swagger so frontend, QA, and other consumers can integrate reliably.

4. How do you typically integrate AI/ML models into a web application?
   I expose trained models via a separate inference service (often Python/FastAPI or Flask) or call third-party APIs (OpenAI, Hugging Face), then build a typed client in the Node backend, adding request validation, rate limiting, retries, and observability around AI calls.

5. What is your experience working directly with AI APIs like OpenAI or Hugging Face?
   I’ve integrated LLMs for tasks like text generation, summarization, classification, and embeddings, handling prompt design, token limits, streaming responses where appropriate, and securely managing API keys via environment variables or secret managers.

6. How do you ensure performance and scalability for AI-driven endpoints?
   I use caching (e.g., Redis) for deterministic or repetitive requests, queue long-running jobs, batch calls when possible, set sensible timeouts, and monitor latency and error rates, scaling horizontally behind a load balancer when traffic grows.

7. How do you test applications that include AI or non-deterministic behavior?
   I isolate AI integration behind an interface, use mocks and fixtures in unit/integration tests, and create contract tests for the AI service; for non-deterministic outputs, I assert on structure and key properties instead of exact wording.

8. What tools and practices do you use for CI/CD and code quality?
   I rely on Git-based workflows, automated tests (unit/integration), linting (ESLint), formatting (Prettier), type checking (TypeScript), and pipelines (GitHub Actions/GitLab CI) that build, test, and deploy to staging/production on tagged or main-branch changes.

9. How do you keep React codebases maintainable as they grow?
   I use a component-driven architecture, co-locate logic and styles, enforce TypeScript types, use custom hooks for shared logic, state management only where needed, and Storybook or similar tools for documenting and testing reusable components.

10. Why are you interested in this AI Fullstack Developer role?
    It aligns directly with my experience in React/Node and RESTful APIs, and lets me go deeper into integrating AI models into real products—exactly where I want to grow: turning modern AI capabilities into reliable, scalable features for end users.

This role is a strong match for a senior full-stack engineer (contract, fully remote) who enjoys building React frontends and Node.js/Express backends with clean MVC architecture and well-designed RESTful APIs, and wants to work closely with AI/ML teams to ship AI-powered features into production. You’ll thrive here if you have 5+ years of experience, are comfortable across TypeScript, SQL/NoSQL databases, CI/CD, and cloud (AWS/GCP/Azure), and have hands-on experience integrating AI models (Python, TensorFlow/PyTorch or OpenAI/Hugging Face APIs) into scalable, maintainable web applications.