HelloFresh

Staff Backend Product Engineer, Shopping AI & Search squad

31.5k - 49.2k PLN31 500 - 49 200 PLN/ mies./ mies.B2BB2B (netto)

SeniorHybryda·WarszawaFull-time·B2B

#442877·Dodano miesiąc temu·37

Źródło: nofluffjobs.com

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Tech Stack / Keywords

Microservices Kafka RabbitMQAWS SQSGo Java Kotlin Python TypeScript Node.jsAICloudAWS KubernetesUXCommunication skills

Firma i stanowisko

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.

Wymagania

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.

Obowiązki

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.

Benefity

Global collaboration with experienced engineers and product partners across international teams.
Opportunity to build and operate AI-powered systems at global scale serving millions of customers.
Technical leadership roles influencing architecture, quality, and autonomous product-led workflows.
End-to-end ownership of decisions from problem definition through production and continuous improvement.
Workspace access with modern facilities including showers, breakout zones, outdoor space, cycle parking, and refreshments (coffee, soft drinks, fruit).

Elastyczne godziny

Karta sportowa

Prysznic

Parking dla rowerów

Napoje w biurze

Darmowe przekąski

HelloFresh

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