Staff Backend Product Engineer (Shopping AI & Search)

Quick Facts

• Role: Staff Backend Product Engineer (Shopping AI & Search)

• Focus: Unified AI Assistant experiences for the shopping journey (chatbot + in-app)

Description

You will lead the technical architecture for AI-powered shopping assistant experiences, including conversational search, personalized search, and proactive in-app recommendations. You’ll make and own end-to-end backend technical decisions for integrating LLM capabilities into customer-facing systems, balancing quality, latency, and cost at scale. You’ll also act as a technical bridge across squads and product/platform stakeholders while driving best practices for building and operating probabilistic AI systems.

Responsibilities

• Lead technical architecture for unified AI assistant experiences across chatbot and broader shopping journey surfaces

• Own end-to-end technical decisions for integrating LLM-powered capabilities via APIs/SDKs

• Maintain architecture coherence between Shopping AI & Search and neighboring squads

• Define engineering best practices for AI systems: observability, graceful degradation, and testing strategies for probabilistic components

• Lead through architecture reviews, design discussions, and hands-on mentoring

• Shape the technical roadmap by identifying architectural bottlenecks early and proposing improvements

Requirements

• 8+ years of backend engineering experience with deep expertise in distributed systems, microservices, and event-driven architecture (Kafka, RabbitMQ, or AWS SQS/SNS)

• Hands-on proficiency in Go (or another modern backend language) with willingness to work in Go

• Practical experience integrating LLMs or AI services into production systems via APIs/SDKs (not model training)

• Ability to design around latency, cost, and non-determinism

• Daily hands-on use of AI coding tools (e.g., Claude Code, Cursor, Copilot)

• Solid understanding of AWS and Kubernetes and observability practices

• Track record of turning ambiguous product problems (search relevance, recommendation quality, conversational UX) into a technical roadmap

• Collaborative, low-ego approach with strong technical judgment and mentoring mindset

• Excellent communication with senior stakeholders and distributed international teams

• Experience with search or recommendation systems is a plus

Benefits

• Global collaboration at scale with a knowledge-sharing culture

• Build and operate AI-powered systems at global scale serving millions of customers

• Technical leadership driving best practices and ways of working in an autonomous, product-led setup

• End-to-end ownership from problem definition through to production

• Workspace access at Mennica Legacy Tower (Prosta 20) with modern facilities