ML Engineer

We're building a next-generation Analytical Platform for a leading resort and entertainment group in Southeast Asia. The platform will combine large-scale data processing, real-time analytics, and machine learning to power decisions in marketing, customer personalization, operations, and risk management.

As a Machine Learning Engineer, you will be responsible for designing, training, and deploying ML models that deliver business value across a wide range of use cases—such as dynamic pricing, customer segmentation, and predictive maintenance.

You’ll collaborate closely with Data Engineers, Data Architects, Business Analysts, and DevOps to bring models from experimentation to production.

Requirements

• 4+ years of experience as a Machine Learning Engineer.
• Proficiency in Python and ML libraries/frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
• Solid understanding of machine learning algorithms, data preprocessing, and evaluation techniques.
• Experience with ML lifecycle management, including versioning, retraining, and monitoring.
• Strong knowledge of data wrangling and feature engineering using large datasets.
• Familiarity with data processing frameworks (e.g., Spark, Pandas, Dask).
• Experience building ML services and APIs for real-time or near real-time applications.
• Understanding of MLOps concepts and working within CI/CD pipelines for ML.
• Advanced level of English & Ukrainian languages

Responsibilities:

• Design, develop, and deploy machine learning models to support business cases like personalization, fraud detection, and customer value prediction.
• Build robust data pipelines for model training and inference using structured and semi-structured data.
• Perform feature engineering, model optimization, and validation using statistical and ML techniques.
• Work with Data Engineers to ensure data availability and quality across the ML lifecycle.
• Use tools for model tracking, versioning, and reproducibility (e.g., MLflow or equivalent).
• Package models for deployment and integrate them with APIs, dashboards, or other systems.
• Monitor and maintain model performance in production and support retraining pipelines.
• Communicate results clearly to technical and non-technical stakeholders.

Benefits

• 35 absence days per year for work-life balance
• Udemy courses of your choice
• English courses with native-speaker
• Regular soft-skills trainings
• Excellence Сenters meetups
• Online/offline team-buildings
• Business trips