We are currently seeking a skilled and experienced Data Scientist to join our team. As a Data Scientist, you will be responsible for experiments with datasets searching for useful insights and features, maintaining data pipelines and training machine learning algorithms, and visualizing results.Main goals and responsibilities:Be a proactive team worker;Collaborate with the team to implement new features to support the growing data needs;Build, maintain, and deploy DS and ML pipelines, including data retrieval, preprocessing, feature engineering, training, and inferencing the models, analyzing and visualizing the results;Build, maintain and deploy LLM-powered systems and agent pipelines, including prompt engineering, tool and API integration, memory and context management, inference orchestration, and evaluation of model outputs;Share knowledge with other teams on various data science or project-related topics;Collaborate with the team to decide on which tools and strategies to use within specific scenarios.What We Offer:Long-term career stability with a competitive salary paid in USD.Conditions for steady career development.Development supported by dedicated mentors and a variety of programs focused on expertise and innovation.Private medical insurance provided after successful completion of the probationary periodA well-equipped and cozy office supports comfort and productivity across all project stages.Welcoming atmosphere and a friendly corporate culture. Required:English level upper intermediate+;Good mathematical and statistical background, tensor calculus;Good knowledge of databases such as Postgres, MongoDB, and SQL;Python language including practical experience with Scikit-learn, Numpy, Pandas, and Matplot libraries;Experience with gradient boosting algorithms (XGBoost or LightGBM);Familiarity with LLM orchestration tools (e.g., LangChain, LangGraph);Experience using or integrating with cloud LLM APIs (e.g., OpenAI, Claude, Amazon Bedrock, Vertex AI);Hands-on experience with Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., pgvector, FAISS, Weaviate, Pinecone);Practical experience with prompt engineering techniques, agentic tools and workflows;At least 1 framework to build and train neural networks (TensorFlow/Keras, PyTorch), a good understanding of neural networks architectures, and how-tos;Commercial experience with classic machine learning and deep learning, including NLP and CV models like Bert, Resnet;Practical skills in building en-to-end ML training pipelines (data load, preprocess, train, inference), as well as GitHub / GitLab CI/CD flows;Docker or Kubernetes platform;Work experience as a Data Scientist more than 2 years.Nice to have:Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP);Ability to work with Spark, Airflow.