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A leading tech firm is seeking an experienced Data Engineer with expertise in Machine Learning to work on enhancing an eCommerce platform. This role requires strong programming skills in Java and Python, along with a deep understanding of search technologies like Lucene, Solr, and Elasticsearch. Responsibilities include developing and optimizing search solutions and building scalable data pipelines. The ideal candidate will have a proven track record of deploying ML systems in production and a solid grasp of software engineering best practices.
The primary goal of the project is the modernization, maintenance and development of an eCommerce platform for a big US-based retail company, serving millions of omnichannel customers each week.
Solutions are delivered by several Product Teams focused on different domains - Customer, Loyalty, Search and Browse, Data Integration, Cart.
Current overriding priorities are new brands onboarding, re-architecture, database migrations, migration of microservices to a unified cloud-native solution without any disruption to business.
We are looking for an experienced Data Engineer with Machine Learning expertise and good understanding of search engines, to work on the following :
5+ years of experience in Data Science or Machine Learning Engineering, with a focus on Information Retrieval or Semantic Search.
Strong programming experience in both Java and Python (production-level code, not just prototyping).
Deep knowledge of Lucene, Apache Solr, or Elasticsearch (indexing, query tuning, analyzers, scoring models).
Experience with Vector Databases, Embeddings, and Semantic Search techniques.
Strong understanding of NLP techniques (tokenization, embeddings, transformers, etc.).
Experience deploying and maintaining ML / search systems in production.
Solid understanding of software engineering best practices (CI / CD, testing, version control, code review).
English : B2 Upper Intermediate
* O salário de referência é obtido com base em objetivos de salário para líderes de mercado de cada segmento de setor. Serve como orientação para ajudar os utilizadores Premium na avaliação de ofertas de emprego e na negociação de salários. O salário de referência não é indicado diretamente pela empresa e pode ser significativamente superior ou inferior.