We are looking for a Machine Learning Engineer to join our growing data team. You’ll design, build, and deploy production-grade ML systems that directly impact business decisions. If you are passionate about solving complex problems with data and building scalable AI solutions — we’d love to meet you.
Responsibilities
- Design, develop, and deploy end-to-end ML systems and pipelines in a production environment.
- Research and identify algorithms that best fit specific business and technical problems.
- Collect, clean, prepare, verify, and perform feature engineering for model training.
- Deploy models, build monitoring systems for performance and data drift detection, and automate model retraining processes
- Run A/B tests, optimize model efficiency and inference speed.
- Work closely with Data Analysts, Data Engineers, and Developers to ensure seamless integration of ML solutions into the product ecosystem.
- Stay up to date with the latest ML algorithms, frameworks, and tools, applying them where beneficial.
Requirements
- 3+ years of experience in applied Machine Learning or Data Science.
- Ability to work independently and take ownership of projects from idea to production.
- Strong programming skills in Python and proficiency with key ML libraries.
- Solid understanding of statistical modeling, feature engineering, and data preprocessing.
- Hands-on experience deploying and maintaining ML models in production.
- Experience with ETL processes and data pipelines.
- Strong knowledge of SQL and working with large datasets.
- Understanding of MLOps principles (model lifecycle, monitoring, retraining).
- Familiarity with cloud environments (AWS, GCP, or Azure) is a plus.
- English level: Intermediate+ for documentation and team communication.
Tech Stack
Core: Python, Scikit-learn, pandas, CatBoost, LightGBM/XGBoost, SQL.
Nice to have: Airflow, MLflow, Docker, Spark.
Bonus: Experience building anti-fraud or spam detection systems.
We offer
- Official employment and support for a work visa.
- Opportunities for professional growth.
- Friendly work environment.
- Flexible start to the workday.