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, Lang Graph/Lang Chain orchestrators) via secure, well-documented APIs (Fast/REST/GraphQL/Web Socket/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 (/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 (., '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 (., cost-aware model selection between Gemini Flash/Pro) through simple, protected 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 / Web Sockets) 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 Fin Ops to optimize Cloud Infrastructure Consumption Cost

BE Computer Science Engineering

8+ Years Experience

Frontend, Self-service and Platform Engineering

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📌 Lead Frontend Engineer (India)
🏢 Ford Motor
📍 India