Staff Backend Product Engineer, Shopping AI & Search squad

HelloFresh is a global technology organization of 1000+ people building the digital products behind food experience including meal kits and specialty offerings. The Warsaw office is part of HelloTech, organized into autonomous, cross-functional alliances owning specific domains. The Shopping AI & Search squad within the Shopping Journey tribe focuses on building unified AI Assistant experiences including conversational search, personalized search, and proactive in-app recommendations to enhance the shopping journey for HelloFresh customers.

• 8+ years of backend engineering experience with expertise in distributed systems, microservices, and event-driven architecture (Kafka, RabbitMQ, or AWS SQS/SNS).
• Strong hands-on proficiency in Go, Java, Kotlin, Python, or TypeScript/Node.js, with willingness to work primarily in Go.
• Practical experience integrating LLMs or AI services into production via APIs/SDKs (not ML engineering).
• Daily hands-on use of AI coding tools like Claude Code, Cursor, or Copilot.
• Solid understanding of modern cloud infrastructure and observability practices, particularly AWS and Kubernetes.
• Ability to translate ambiguous product problems (search relevance, recommendation quality, conversational UX) into technical roadmaps.
• Collaborative, low-ego approach with strong technical judgment and mentoring capabilities.
• Excellent communication skills and experience working with senior stakeholders and distributed international teams.

Nice to have:

• Prior experience with search or recommendation systems.

• Lead the technical architecture for unified AI Assistant experiences spanning conversational search, personalized search, and in-app recommendations.
• Own end-to-end technical decisions integrating LLM-powered capabilities into customer-facing systems balancing quality, latency, and cost.
• Act as the technical bridge between squads and stakeholders to maintain coherent architecture as the assistant expands.
• Define and drive engineering best practices for AI-powered systems including observability, degradation, and testing strategies.
• Provide technical leadership through architecture reviews, design discussions, and mentoring engineers.
• Use and promote AI coding tools (e.g., Claude, Cursor) as part of engineering practices.
• Shape technical roadmap by identifying and resolving architectural bottlenecks early.