Fujitsu’s R&D vision is to create cutting-edge technologies that support society and prioritize the flow of data. Our five key technologies are essential elements for collecting data from all parts of society, transporting it over ultra-high-speed and secure networks, analyzing it with trusted AI, converting it into value, and returning it to society. Central to realizing our strategy and vision are the people behind our R&D initiatives. We strive to foster top-notch talent in the innovation process by nurturing skilled individuals equipped to advance this cause with originality and dedication.
Object:
To build a lightweight full-stack and enterprise grade web application for an AI-enabled MVP which is scalable to several domains. The application will include multiple core modules based on provided design
files and a CSS/design system, along with backend services for data handling, API integration, and AI workflow enablement.
Scope of Work:
Frontend
• Build responsive, pixel-perfect React UIs using provided designs and design system standards.
• Create reusable components (layouts, navigation, tables, forms, modals) aligned with the design system.
• Set up routing, state management, and API data flows, supporting all user workflow states.
• Ensure accessibility (WCAG AA), optimize performance, and support cross-browser compatibility.
• Write unit and integration tests for critical UI features; add basic end-to-end tests as needed.
• Use TypeScript and modern React best practices to keep code clean, documented, and linted.
• Design agentic UI/UX for AI workflows, including guided steps, suggestions, approvals, and feedback.
• Leverage modern frontend tools/libraries: React, TypeScript, Vite/Webpack, Redux/React Query, Tailwind/Material UI, and Jest/RTL/Cypress.
• Adapt quickly to new frameworks, patterns, and changing requirements.
Backend
• Design and develop RESTful APIs using Python (FastAPI/Flask) or Java (Spring Boot)
• Implement data models and service layers
• Handle API integration for frontend consumption
• Set up mock/real database (PostgreSQL/MySQL or NoSQL if needed)
• Ensure secure, scalable, and maintainable backend architecture
• Build AI-facing backend capabilities such as inference request/response contracts, model configuration management, and integration with model-serving endpoints
Full-Stack Responsibilities
• Integrate frontend with backend services
• Manage API contracts and data flow
• Handle basic deployment setup (local/cloud-ready)
• Collaborate on system design and architecture decisions
• Work with AI/ML teams on AI product development: model integration, prompt/inference workflows, evaluation hooks, and release readiness
• Support model hosting and serving patterns (e.g., containerized model servers, GPU/CPU deployments, autoscaling) and expose inference via secure APIs
Deliverables:
• Complete full-stack application with key UI modules and end-to-end workflows
• Backend APIs supporting dashboard and blueprint workflows
• Reusable component library aligned with design system
• Mock/real data integration across frontend and backend
• Clean, maintainable codebase (Vite + React + Backend framework)
• AI integration deliverables: inference endpoints wired end-to-end, configurable model versions, and basic monitoring/logging for requests, latency, and failures
• Documentation for setup, API usage, and future scalability
Required Skills:
• Strong experience in React.js
• Proficiency in Python (FastAPI/Flask) or Java (Spring Boot)
• Experience with REST API development and integration
• Familiarity with databases (SQL/NoSQL)
• Understanding of responsive design and UI/UX principles
• Hands-on experience integrating AI/ML services (e.g., calling inference endpoints, handling image/video payloads, or working with AI pipelines)
• Familiarity with model serving/model hosting concepts (REST/gRPC inference, containerized serving, basic MLOps/monitoring)
Nice to Have:
• Experience building AI-enabled products (computer vision, LLMs, or ML services) from prototype to production
• Knowledge of cloud platforms (AWS/Azure/GCP)
• Familiarity with CI/CD pipelines
• Experience with containerization (Docker)
• Exposure to model deployment/hosting stacks (e.g., Kubernetes, model gateways, feature flags for model rollout, observability)