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.

Responsibilities:

We are looking for an experienced Data Engineer with Machine Learning expertise and good understanding of search engines, to work on the following:
- Design, develop, and optimize semantic and vector-based search solutions leveraging Lucene/Solr and modern embeddings.
- Apply machine learning, deep learning, and natural language processing techniques to improve search relevance and ranking.
- Develop scalable data pipelines and APIs for indexing, retrieval, and model inference.
- Integrate ML models and search capabilities into production systems.
- Evaluate, fine-tune, and monitor search performance metrics.
- Collaborate with software engineers, data engineers, and product teams to translate business needs into technical implementations.
- Stay current with advancements in search technologies, LLMs, and semantic retrieval frameworks.

Mandatory Skills Description:

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).

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