锦鲤求职岗位描述
Own and drive the frontend delivery for AI Platform web experiences, including translating requirements into scalable UI features, defining acceptance criteria, and ensuring consistent user outcomes across releases. Define, maintain, and continuously improve frontend quality baselines, CI/CD quality gates, and release readiness criteria (e.g., test coverage, accessibility, performance budgets, bundle size thresholds). Design and implement robust frontend architecture and reusable component frameworks, including integration patterns for REST/GraphQL APIs, state management, and regression-safe UI composition. Analyse frontend performance and runtime behaviour (rendering efficiency, bundle size, network usage, browser compatibility), identify constraints, and provide actionable recommendations to improve user experience and reliability. Establish frontend engineering standards, reusable patterns, best practices, and governance for UI quality (clean architecture, code review standards, testing conventions, design-system alignment). Support build-out of new AI platform capabilities in the UI, including experiences that integrate LLM services, model-serving APIs, orchestration workflows, retrieval/search, and other integration layers—ensuring secure, usable, and accessible interactions. Collaborate with stakeholders to assess release risks and provide engineering insights for production readiness decisions, including monitoring signals, incident learnings, and continuous improvement actions. Strong experience (5yr+) building modern web applications with React and TypeScript, ideally for enterprise-scale platforms or complex, data-heavy products. Proven hands-on ability to deliver maintainable UI architecture and high-quality user experiences. Strong engineering (5yr+) fundamentals in HTML5, CSS3, and modern JavaScript (ES6+), with solid understanding of component design, state management patterns (e.g., React Context, Redux, Zustand), and asynchronous data flows. Experience integrating frontend applications with backend services and APIs (REST and/or GraphQL), including handling authentication/authorisation flows, error handling, caching, and resilience patterns for real-world production usage. Demonstrates an AI-native mindset, applying AI-driven approaches to improve productivity, code quality, and decision-making (e.g., faster prototyping, better testing, smarter debugging). Experienced in leveraging coding assistants (e.g., AI pair-programming tools) to accelerate development, enhance code quality, and support engineering best practices (clean architecture, testing discipline, review quality). Experience building frontend experiences for AI/ML platforms, including UIs for LLM services, GenAI applications, model-serving APIs, RAG workflows, vector search, and AI orchestration/agent tooling. Experience with observability and monitoring for web applications and production support, such as Grafana dashboards, AppDynamics, OpenTelemetry, and/or Real User Monitoring (RUM). Experience working effectively with cloud-based environments (GCP, AWS, Azure, or private cloud), and understanding how frontend deployments interact with CDN/caching, edge routing, and environment configuration. Experience building reusable frontend frameworks (component libraries/design systems), shared tooling (linting, build templates), and quality dashboards (e.g., test coverage, performance budgets, accessibility metrics).