Staff Backend Engineer ConsumerActive

HelloFresh
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The opportunity

Join HelloFresh in Warsaw as part of HelloTech, our global technology organisation of 1000+ people building the digital products behind our end-to-end food experience from meal kits and ready-to-eat meals to specialty offerings like pet food and premium meat & seafood. Our…

What you'll do

  • Lead the technical architecture for unified AI Assistant experiences spanning: conversational search, personalized search, and proactive in-app recommendations — so the experience holds together whether the customer is in the chatbot or elsewhere in the app.

  • Own end-to-end technical decisions around integrating LLM-powered: capabilities into customer-facing systems, weighing quality, latency, and cost at scale.

  • Act as the technical bridge between Shopping AI & Search, neighboring squads: across Shopping Journey, and product/platform stakeholders, so architecture stays coherent as the assistant expands to new surfaces.

  • Define and drive engineering best practices for building and operating: AI-powered systems — observability, graceful degradation, and testing strategies for components that are inherently probabilistic.

  • Provide technical leadership through architecture reviews, design: discussions, and hands-on mentoring that raises the engineering bar across the tribe.

  • Use AI coding tools (e.g. Claude, Cursor) as a standard part of how you: build, and help shape how the wider team adopts AI-assisted engineering practices.

What they're looking for

  • Shape the technical roadmap by spotting architectural bottlenecks early and: proposing improvements before they become blockers.
  • + years of backend engineering experience, with deep expertise in distributed: systems, microservices, and event-driven architecture (Kafka, RabbitMQ, or AWS SQS/SNS).
  • Strong hands-on proficiency in Go, or another modern backend language: (Java/Kotlin, Python, or TypeScript/Node.js), with the willingness to work in Go, our squad's primary stack.
  • Practical experience integrating LLMs or other AI services into production: systems via APIs/SDKs. This isn't an ML engineering role — we're not training foundation models — but you're comfortable designing around how they behave: latency, cost, and non-determinism.