Lead Frontend Engineer

Ford Motor Company

Location: Chennai, Tamil Nadu, India
Job type: Full-time

Required skills

LangChain
Apigee
backend
BigQuery
caching
cloud infrastructure
compliance
end-to-end
FastAPI
frontend
full-stack
GitHub
GraphQL
JS
Jest
Kubernetes
multi-tenant
OAuth2
React
reference architecture
Reference Architecture
SaaS
SRE
TypeScript
user feedback
WCAG
Websockets
Vertex

About the role

Ford Motor Company

Website: ford.com
Job details:
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 Click on Apply to know more.

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Lead Frontend Engineer

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