Lead Frontend Engineer

Description

• Own end-to-end frontend architecture for AI and Non-AI Platform products, defining patterns for streaming UI, real-time agent responses, tool-call visualization, citations/grounding display, and multi-turn conversational state.
• Define and enforce the frontend technology stack, coding standards, component architecture (design system), and state-management strategy across all AI Platform UI surfaces.
• Architect the integration layer between frontend applications and AI backend services (Vertex AI / Gemini, Google ADK agents, RAG pipelines, LangGraph/LangChain orchestrators) via secure, well-documented APIs (Fast/REST/GraphQL/WebSocket/SSE).
• Drive technical decisions on rendering strategy (SSR/CSR/ISR), edge/streaming delivery, performance budgets, and accessibility (WCAG 2.1 AA) for AI-heavy, latency-sensitive interfaces.
• Design and maintain the AI Platform's internal Developer Portal / Self-Service Console, enabling engineering teams to provision AI resources (RAG pipelines, agents, vector indices, environments) without manual tickets — extending self-service cloud infrastructure provisioning patterns into the UI layer.
• Build and version a reusable AI-UI Component Library / Design System (chat widgets, agent trace viewers, citation cards, feedback/human-in-the-loop widgets, prompt/response inspectors) consumable by multiple product squads across the enterprise as a shared platform capability.
• Define 'golden path' frontend templates and starter kits (Next.js/React + AI SDK boilerplate) so downstream product teams can spin up new AI-powered UIs in days, not weeks — a core platform-engineering productivity outcome.
• Own the frontend layer of platform Service Catalog entries (e.g., 'Conversational Agent UI', 'Document Intelligence Console') including golden-path templates, scorecards, and documentation, aligned to internal developer-platform (IDP) practices.
• Partner with platform/infra teams to expose infrastructure and model-routing configuration (e.g., cost-aware model selection between Gemini Flash/Pro) through simple, safe self-service UI controls rather than backend-only configuration.
• Establish frontend observability (RUM, tracing of AI response latency, token-stream monitoring) integrated with platform-wide SRE/observability tooling leveraging Dynatrace SaaS.
• Build production-grade full-stack features spanning UI, BFF (Backend-for-Frontend) layer, and orchestration services that call LLM/agent backends (Vertex AI Gemini, Google ADK multi-agent workflows).
• Implement secure, low-latency streaming pipelines (Server-Sent Events / WebSockets) for token-by-token AI responses, agent 'thinking' states, and tool-execution progress indicators.
• Integrate authentication/authorization (OAuth2 client credentials, API Gateway via Apigee X) into frontend session and API-call flows, ensuring B2B and B2E security compliance.
• Collaborate with backend/platform engineers on API contracts, chunking/retrieval metadata exposure (for citation rendering), guardrail/response-safety signal surfacing, and error/fallback UX for model or agent failures.
• Evaluate and select appropriate technologies and tools to support the development and deployment of products on the eCommerce foundation layer
• Collaborate with stakeholders to understand business needs and requirements, and translate them into technical and non-functional specifications
• Work with Product teams to understand their pain points and increase the Developer Experience through Platform Engineering Capabilities
• Strategize & work with senior leaders across Ford’s Enterprise Architecture, IT Operations to make significant, measurable impact on the eCommerce Platform
• Experience in Cloud FinOps to optimize Cloud Infrastructure Consumption Cost

Responsibilities

• AI-Native Chat & Agent UI: Production conversational interface with streaming responses, multi-agent trace visualization, citation/grounding display, and tool-call transparency, built with React 18+/Next.js (App Router, Server Components/Actions).
• AI SDK Integration Layer: Frontend integration React.js-Node.js (Typescript) / LangChain.js / Google ADK web clients for streaming completions, function-calling UI, and structured output rendering.
• Design System & Component Library: Versioned, themeable component library (Storybook-documented) with AI-specific components — chat bubbles, agent step timelines, feedback thumbs-up/down + RLHF capture widgets, prompt inspector, retrieval-source cards.
• Real-Time & Streaming UX: SSE/WebSocket-based streaming UI patterns, optimistic UI, token-level rendering, and graceful degradation/fallback states for model latency or failure.
• State Management: Robust conversation/session state handling using modern state libraries supporting multi-turn context and agent memory visualization.
• Accessibility & Performance: WCAG 2.1 AA compliant, Core Web Vitals-optimized AI UI, with performance budgets for time-to-first token and time-to-interactive.
• Testing Suite: Component (Jest/RTL), E2E (Playwright) test suites including deterministic mocking strategies for non-deterministic LLM outputs.
• Developer Self-Service Portal UI: Internal web console for provisioning AI resources, viewing agent/RAG pipeline health, and managing environment configs, aligned to Cloud Platform self-service provisioning.
• BFF / API Layer: Backend-for-Frontend service (FastAPI / Node.js) mediating between UI and AI backends, handling auth, request shaping, response streaming, and caching.
• Agent & RAG Orchestration Integration: Full-stack wiring to Google ADK multi-agent workflows (intent classification, routing, guardrails) and Vertex AI RAG Engine / Gemini for grounded, cited retrieval-augmented responses.
• Vector Search & Retrieval UI-to-Backend Contract: End-to-end pipeline exposing embeddings/vector search results (BigQuery, Vertex Vector Search) with chunk-level metadata surfaced in the UI for source citation.
• Secure API Gateway Integration: Full-stack OAuth2 (client-credentials) authentication flows through Apigee X / API Gateway, including token refresh, session handling, and role-based UI rendering.
• Cost-Aware Model Routing UI: Full-stack feature exposing dynamic LLM routing (e.g., Gemini Flash vs. Pro) based on query complexity, with a UI toggle/telemetry dashboard for cost and latency visibility.
• Deployment on Kubernetes (GKE): Containerized frontend and BFF services with CI/CD pipelines (GitHub Actions/Cloud Build), deployed via GKE with progressive delivery (canary/blue-green) and environment promotion aligned to platform standards.
• Observability & Feedback Loop: Full-stack telemetry capturing latency, token usage, guardrail triggers, and user feedback, feeding back into model/prompt evaluation dashboards.
• Reference Architecture & Golden Path Repo: A reusable, documented starter template (frontend + BFF + agent integration) published to the internal platform catalog for rapid onboarding of new AI product teams.
• Platform-as-a-Product mindset: Define and track adoption, developer satisfaction (DevEx/NPS), and time-to-first-deploy metrics for teams consuming the AI UI SDK/component library.
• Service Catalog ownership: Publish and maintain frontend 'golden path' templates as catalog entries (e.g., via Backstage-style internal developer portal) with clear SLAs, ownership, and versioning.
• Self-Service Enablement: Extend self-service cloud infrastructure provisioning into the presentation layer — enabling product teams to self-provision chat UIs, agent consoles, and dashboards without platform-team tickets.
• Reusability & Multi-Tenancy: Architect UI components and BFF services to be multi-tenant and configuration-driven so multiple business units/products can adopt the same platform UI foundation.
• Governance-as-Code: Embed responsible-AI, security, and accessibility guardrails directly into shared components and CI pipelines so consuming teams inherit compliance by default.
• Platform Roadmap & Feedback Loops: Run regular platform reviews with consuming teams to prioritize the AI-UI roadmap, deprecations, and versioning strategy.

BE Computer Science Engineering

8+ Years Experience

Frontend, Self-service and Platform Engineering