Overview
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
- 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.
Must have
- 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
- 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.