in Sber's ATM network development team: development of AI agents and LLM systems, classic ML and NLP tasks, data pipelines, and solution quality assessment.
Tasks:
- Develop and advance AI agents and LLM systems: build pipelines, experiment with models and approaches, assess quality, and improve solutions.
- Solve classic ML and NLP tasks: classification, working with texts, embeddings, and other ML models.
- Conduct experiments with models and their parameters, analyze results, and select the best approaches.
- Develop data preparation and processing pipelines for ad-hoc analytics and data mart construction.
- Organize and conduct quality assessment of ML/LLM solutions, work with data labeling.
- Participate in solution development at all stages: from task definition and client communication to deployment and production monitoring.
Requirements:
- Understanding of AI agent construction principles.
- Proficient in Python and SQL, ability to work with Git.
- Understanding of the main stages of ML solution development.
- Knowledge of classic ML basics: classification, regression, main algorithms, and quality metrics.
- Understanding of main NLP approaches and text data handling.
Conditions:
- Comfortable modern office near Kutuzovskaya metro station (office or hybrid work mode).
- Corporate gym and recreation areas.
- Sber's unique training system for professional and career development, seminars, trainings, conferences.
- Adaptation program and mentor support at the start.
- Voluntary medical insurance, accident insurance, social guarantees.
- Flexible mortgage discount, equal to 1/3 of the Central Bank's key rate.
- Prime subscription with the ability to share with three close individuals.
- Reward for recommending friends to the Sber team.
- Corporate pension program.
- Corporate events.
Skills:
- Python
- SQL
- Git
- ML
- NLP
- LLM
- RAG
- Hadoop
- Spark
- MLOps
- Docker
- Jenkins
- Linux
