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 Platforms 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 ( 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 Fords 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/ (App Router Server Components/Actions).
• AI SDK Integration Layer: Frontend integration (Typescript) / / 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 / ) 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