- Development of solutions to speed up work with ML experiments and processing large amounts of data- Optimization and development of ML infrastructure that ensures effective work of teams within the company- Ensuring the reliability of ML processes: delivery, data synchronization, infrastructure deployment- Creation of new tools and approaches to the delivery of ML models before sale- Integration with cloud platforms: AWS / GCP

- Collecting functional and non-functional requirements for tasks and projects- Working through User Story and Use Cases- Writing and designing code for Clean Architecture, DDD / LDDD. If you need it very quickly, then layered- Writing a RESTful API on Level 2 of the Richardson Maturity Model- Monitoring the quality of the code quantitatively using metrics and communicating information about the technical debt to interested parties- Ensuring a loosely connected architecture with proper domain partitioning- Not indifferent to abbreviations and words KISS, DRY, YAGNI, SOLID, coupling / cohesive, connascenceTDD on obscure domain sites- Writing ADR for architectural solutions- Organizing code by Git Flow - I made the transition from the "hourglass" of testing to the testing pyramid- Refactoring on "bloody hell" sections of the code- Management of a group of 2 backend developers

- Generation of microservices by declarative description (Platform-as-a-Service)- Pretty diverse set of technologies: Java / Kotlin, Spring, Go, Python, Terraform, Grafana, Kibana, Prometheus, OpenAPI, Postman, Mockk, Jaeger, Elasticsearch

Social Media Automation Tool

Some staff with money