ML Engineer

Grid Dynamics is a technology consulting, platform and product engineering, AI, and advanced analytics services provider that helps enterprise companies navigate business transformation by combining technical vision with business acumen.

• Own the end-to-end DS/ML project lifecycle from initial research to robust production deployment phases;
• Build and sustain deployable statistical and machine learning models, primarily for time series forecasting in the context of integrated business planning;
• Manage and optimize legacy code while collaborating with core developers and business-side stakeholders;
• Design, implement, and enhance high-quality feature engineering pipelines and data processes.

• Proven ability to successfully transition, deploy, and scale statistical and ML models in a production environment;
• Deep proficiency in Python and PySpark;
• Experience with model lifecycle tracking tools like MLflow;
• Strong hands-on experience with cloud development using Azure Databricks, Azure Data Factory, and Azure DevOps;
• Solid understanding of ML algorithms and core Python packages including scikit-learn, XGBoost, LightGBM, pandas, NumPy, and SciPy;
• Strong knowledge of feature engineering;
• Solid software engineering background with comfort working on legacy codebases.

• Opportunity to work on bleeding-edge projects;
• Work with a highly motivated and dedicated team;
• Competitive salary;
• Flexible schedule;
• Medical insurance and sports benefits;
• Corporate social events;
• Professional development opportunities;
• Well-equipped office.