Senior Data Scientist / ML Engineer (Semantic Search)

Project Description:

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.

Languages:

English: B2 Upper Intermediate