ML Engineer

We are a team of Machine Learning Engineers focused on turning ideas and prototypes into reliable, production-ready systems. We own the end-to-end lifecycle of ML solutions — from integration with existing services to deployment, monitoring, and continuous improvement — ensuring our models deliver stable, measurable value in real-world conditions

Key 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

Skills, Knowledge and Expertise

• 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

• Stable salary, official employment

• Health insurance

• Hybrid work mode and flexible schedule

• Access to professional counseling services including psychological, financial, and legal support

• Discount club membership

• Diverse internal training programs

• Partially or fully paid additional training courses

• All necessary work equipment