Data scientist Java expertise

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• 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, and Cart.
• Current overriding priorities are new brands onboarding, re-architecture, database migrations, and 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 a 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.

• 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, and 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).

• Experience of work in distributed teams, with US customers
• Experience with LLMs, RAG pipelines, and vector retrieval frameworks.
• Knowledge of Spring Boot, FastAPI, or similar backend frameworks.
• Familiarity with Kubernetes, Docker, and cloud platforms (AWS/Azure/GCP).
• Experience with MLOps and model monitoring tools.
• Contributions to open-source search or ML projects.

Mid-Senior level

Full-time

Information Technology

IT Services and IT Consulting