Design and develop ML modules for various products — from experimentation and prototyping to production integration
Participate in ML solution architecture, define technical approaches, and establish engineering standards
Prepare and process data, train models, conduct A/B tests, and analyze results
Monitor and improve model quality, interpret model behavior and key metrics
Collaborate with product managers, engineers, and analysts to clarify requirements, integrate solutions, and evaluate their effectiveness
Work with MLOps infrastructure: CI/CD, monitoring, logging, and containerization

3+ years of experience in ML engineering and building production-grade machine learning systems
Strong expertise in NLP, LLMs, AI assistants, spam detection, behavioral analysis, and related areas
Proven experience delivering end-to-end ML solutions — from idea and data preparation to production deployment and ongoing support
Excellent Python skills and experience in industrial software development
Strong understanding of A/B testing principles and result analysis
Experience making architectural decisions and driving engineering best practices
Hands-on experience with Docker, Kubernetes, CI/CD systems, monitoring tools (e.g. Prometheus, Grafana), and logging solutions
Will be a plus: Product mindset and understanding of business metrics
Experience building real-time and high-load ML systems
Familiarity with modern MLOps tools such as MLflow, Airflow, and Kubeflow

Machine Learning Engineer

Key skills