• Analyze large and complex datasets from unstructured data, logs, and production records.
• Build LLM-based applications such as Retriever-Augmented Generation (RAG) to support different teams within the organization.
• Conduct and contribute to experiments, write reusable code.
• Deploy ML/DL models into production.
• Work closely with operation and production teams to understand their data needs and provide tailored analytical solutions.
• Collaborate with cross-functional teams within the Data Science, Data Engineering, and Data Analytics divisions to ensure data pipelines, models, and analyses are aligned with business needs.

We offer

• 5/2, 09.00-18.00;
• Meal allowance;
• Annual performance bonuses;
• Corporate health program: VIP voluntary insurance and special discounts for gyms;
• Access to Digital Learning Platforms.

• Bachelor or Master’s degree in Computer Science, Engineering, Information Technologies or related fields.
• 2+ Years of experience as Data Scientist, Deep Learning Specialist, Machine Learning Engineer or equivalent
• Strong knowledge in Python programming language with OOP methods
• Deep understanding of the mathematics behind modern machine learning, linear algebra, and statistics
• Strong understanding in Classical Machine Learning and Deep Learning Models
• Experience with Deep Learning frameworks - (PyTorch or Tensorflow)
• Experience with Computer Vision applications or Natural language Processing Applications.
• Experience with Git
• Strong understanding of State-of-the-art architectures
• Good Understanding of Supervised, Unsupervised techniques for Deep learning models.
• Good Understanding of Deployment tools - Docker, Airflow
• Good Understanding of Computer Architectures
• Strong communication skills with ability to explain complex technical concepts to non-technical stakeholders

Preferred Qualifications

• Experience with LLM, RAG
• Publish of Academic Paper
• Experience with on-prem methodologies

Note: Only candidates who meet the requirements of the vacancy will be contacted for the next stage.

Interested candidates can apply by clicking the link provided in the "Apply" button.