AI Engineer

You'll work on agent / multi-agent AI systems for customer experience and personalization, including:

●      Designing and implementing Agentic AI pipelines — tool-calling, planning, memory, and multi-step workflows that power use cases like personalized product recommendation, beauty consultation, conversational shopping, and proactive customer engagement.

●      Working with CDP & customer data — leveraging unified customer profiles, purchase history, browsing behavior, and segmentation from the CDP; managing logs, user feedback, synthetic data, and evaluation traces to drive iteration and improvement.

●      Writing RAG / finetuning / post-training code — implementing RAG over product catalogs and customer knowledge, finetuning, and post-training components (e.g., rerankers, evaluators, safety/brand-guardrail layers) optimized for throughput, latency, and cost at eCommerce scale.

●      Integrating agents with real systems — connecting agents to internal services (CDP, CRM, recommendation engines, order/product systems) and external APIs; embedding them into real customer touchpoints across web, app, and messaging channels.

●      Evaluating agents on real customer tasks — measuring success rate, conversion impact, latency, robustness, and behavior in production; closing the loop between evaluation, debugging, A/B testing, and improvements to deliver measurable business value.

●      2+ years working with Agentic AI / LLM agents / multi-step tool systems, or closely related experience.

●      Strong coding skills in Python (or similar) and experience building backend or ML pipelines.

●      Comfortable working with APIs, services, and internal/customer data sources; familiarity with customer data platforms, CRM, or personalization systems is a plus.

●      Experience with streaming data and modern LLM/agent frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel) is a strong plus.

●      Experience in eCommerce, retail, or customer-facing products is preferred.