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.