Senior Data Scientist/ AI Engineer (m/w/d) 100% remote

Project Details

• Start Date: 04.05.2026
• Duration: 31.12.2026
• Employment Type: full_time
• Location: 100% remote
• Project Language: English

Project Description

This project involves the creation of an in-house solution for on-site search and a Merchandising UI, aiming to replace the current keyword-driven external solution with an AI-driven solution featuring semantic understanding and personalization capabilities.

Responsibilities:

• Technical consultation, configuration, and optimization of OpenSearch-based search relevance components, including analyzers, scoring parameters, hybrid retrieval structures, and vector-search integrations.
• Development and refinement of retrieval and ranking models, including multi-stage ranking approaches, learning-to-rank concepts, and the integration of relevance, behavioral, and business signals into ranking pipelines.
• Creation of multilingual NLP components for various locales (LAM, EU, NAM), including tokenization, stemming, normalization, and configuration of locale-specific linguistic processing within OpenSearch and related pipelines.
• Design and implementation of query understanding mechanisms, including synonym and concept extraction based on catalog and interaction data, query-intent interpretation methods, and term- and concept-expansion techniques based on contractors expertise.
• Development of LLM-enhanced search components, including prompt construction and the incorporation of LLM-derived semantic signals into retrieval and ranking logic.
• Creation of personalization logic for search and PLP ranking, including re-ranking frameworks that balance user affinity, semantic relevance, and commercial parameters.
• Development and validation of autocomplete, spelling-correction, and search-suggestion components, to enable robust multilingual handling and adherence to domain-specific terminology.
• Definition and refinement of commercial relevance models, including recency-weighted popularity signals, interaction-derived relevance indicators, and other business-driven ranking adjustments.
• Construction of evaluation and diagnostic frameworks for relevance quality, employing offline IR metrics (information retrieval) and analytical methods for relevance assessment.
• Modeling and integration of real-time or near-real-time data signals including size availability or stock levels into filtering, faceting, and ranking components.
• Technical implementation of data-science-driven backend logic for the Merchandising UI, including scoring routines, rule-evaluation structures, configurable business logic, and interfaces that enable merchandise-related adjustments to search and ranking behavior.

Skills Required:

• Developing a next-generation, AI-driven on-site search and PLP-ranking capability.
• Specialized expertise in OpenSearch relevance engineering.
• Multimodal product embeddings.
• Semantic search optimization.
• Personalization models.
• LLM-based query processing.
• Hybrid lexical/semantic retrieval.
• Multilingual search infrastructure that is not available internally.