Full-Stack AI Engineer - AI Ecommerce Shopping Assistance

Full-Stack AI Engineer Focus: Agentic AI, AI Shopping Assistants & Virtual Try-On Role Summary We are looking for a Full-Stack AI Engineer to build, deploy, and operate production-grade AI applications, including agentic shopping assistants and multimodal AI experiences. The role requires strong hands-on expertise in Python, TypeScript, LLM-based applications, real-time streaming, evaluation, and cloud infrastructure. The ideal candidate can independently deliver end-to-end features across backend agent runtimes and frontend interfaces, with a strong focus on reliability, latency, cost optimisation, and production performance. Key Responsibilities AI Agent Development: Build and maintain Python-based agent runtimes using LLMs, tool calling, structured outputs, retrieval-augmented generation (RAG), multi-turn state, and human escalation workflows.Full-Stack Engineering: Develop asynchronous Python APIs and TypeScript web components, implementing server-sent events (SSE), streaming interfaces, interactive product cards, and resilient client-side error handling.LLM Evaluation: Build evaluation datasets and automated test harnesses using deterministic and model-based graders, regression gates, ablation studies, and pass@k/pass^k metrics.Performance & Cost Optimisation: Improve response latency, time to first token, token consumption, caching efficiency, context management, and cost per conversation.Production Operations: Implement distributed tracing, structured logging, LLM observability, monitoring, incident resolution, feature flags, controlled rollouts, and rollback mechanisms.Cloud & DevOps: Deploy containerised services, manage CI/CD pipelines, infrastructure as code, and production workloads on cloud platforms.E-commerce AI Integration: Integrate product search, catalogue data, product details, grounded recommendations, and commerce-related event tracking into AI-powered shopping experiences.Virtual Try-On & Multimodal AI: Contribute to image-based generative AI applications, including embeddable frontend widgets and scalable backend services.Internationalisation & Scalability: Support multilingual experiences, brand-specific configurations, localisation, currency handling, and backward-compatible integrations.Cross-Functional Collaboration: Work with product, business, and engineering stakeholders to troubleshoot quality issues and deliver measurable improvements to customer experience and business outcomes. Essential Skills & Experience Strong hands-on experience with Python and TypeScript.Production experience building LLM-powered applications using tool/function calling, structured outputs, streaming, retries, context management, and RAG.Strong understanding of asynchronous Python, REST APIs, SSE, and event-driven communication.Practical experience designing LLM evaluation frameworks, test datasets, automated graders, regression testing, and evaluation metrics.Experience deploying and operating cloud-based applications, containerised services, CI/CD pipelines, and infrastructure as code.Familiarity with production observability, distributed tracing, structured logging, performance monitoring, and incident management.Experience with caching, latency optimisation, token and inference cost management, feature flags, and safe deployment strategies.Strong software engineering fundamentals, debugging skills, and end-to-end ownership of production features. Preferred Skills Experience with agent frameworks such as Google ADK, LangGraph, or Claude Agent SDK.Hands-on exposure to Google Cloud Platform, Vertex AI, Gemini, FastAPI, and Cloud Run.Experience with frontend frameworks and tools such as Lit, Next.js, esbuild, and Vitest.Familiarity with PostgreSQL, BigQuery, OpenTelemetry, Opik, and LLM tracing platforms.Knowledge of MCP servers, sub-agents, agent-to-agent integrations, and retrieval systems.Experience with e-commerce search, multimodal AI, image generation, virtual try-on, and third-party web embeds.Understanding of prompt-injection mitigation, multilingual applications, and inference performance optimisation.Effective use of AI coding assistants and developer productivity tools. Role Expectations Deliver production-ready features across both backend AI services and frontend applications.Demonstrate evaluation evidence, observability, and safe rollout practices for AI-driven changes.Balance response quality, reliability, latency, infrastructure costs, and user experience.Work effectively within existing systems, maintaining backward compatibility and planning migrations and rollbacks.Take ownership of troubleshooting, continuous improvement, and production reliability.