Responsibilities

• Build and deploy machine learning solutions from end to end, contributing to the full ML delivery cycle
• Conduct data analysis, annotation, and processing as a key part of the ML system design
• Design solutions using common patterns and tools, and propose alternative approaches when needed
• Ensure solutions meet design standards and are reusable, flexible, and extensible through peer reviews
• Deploy features and ensure they work as intended while preventing unintended side effects
• Document technical solutions and ensure all necessary monitoring and support tools are in place
• Solve issues in the engineering design and delivery process and ensure the solution meets key performance indicators and non-functional requirements
• Collaborate with team members, contribute to a community of practice, and promote the reuse of common approaches and technologies

Qualifications

• Proficiency in Python and its frameworks for streaming, batch, and asynchronous data processing
• Solid experience with classic machine learning techniques and algorithms
• Experience using MLOps tools and practices to manage the ML model lifecycle
• Familiarity with technologies like Golang for backend system and infrastructure integration
• Ability to conduct thorough data analysis and preparation
• Strong problem-solving skills with a focus on engineering principles and data-driven reasoning
• Excellent communication and collaboration skills, with an ability to seek and validate information from various sources
• A solid understanding of business value and how it connects to delivering features
• A proactive approach to self-development and learning new skills and best practices

Conditions & Benefits

• Stable salary, official employment
• Health insurance
• Hybrid work mode and flexible schedule
• Relocation package offered for candidates from other regions
• Access to professional counseling services including psychological, financial, and legal support
• Discount club membership
• Diverse internal training programs
• Partially or fully payed additional training courses
• All necessary work equipment