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).
nice-to-have skills description:
* 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.