ML Engineer

New

Candidate Work Type: ['Remote']; Job Location: ['Georgia', 'Moldova', 'Poland']Full-TimeJunior

Salary not disclosed

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Job Details

Languages: English at B2 level or higher
Experience: 1+ year of hands-on Python development experience
Required Skills: DockerPythonSQLMachine LearningNumpyPandasRESTful APIsscikit-learn

Have 1+ year of hands-on Python development experience.
Have experience with pandas, NumPy, scikit-learn, and Jupyter.
Have a strong foundation in statistics, probability, and linear algebra, and skills in exploratory data analysis.
Understand supervised and unsupervised learning.
Have practical experience with regression, classification, clustering, dimensionality reduction, and anomaly detection.
Have experience preparing structured datasets, including cleaning, transformation, encoding, scaling, and missing-value handling.
Have practical feature-engineering and feature-selection skills.
Understand train/validation/test splits, cross-validation, data leakage, overfitting, and regularization.
Be able to select evaluation metrics, compare models, and perform error analysis.
Be familiar with hyperparameter tuning and reproducible machine-learning pipelines.
Have SQL skills for data extraction and analysis.
Be able to expose models through REST APIs or batch-processing workflows.
Be familiar with Git, automated testing, Docker, and basic model monitoring.
Be able to explain model behavior, assumptions, limitations, and results.
Have English proficiency at B2 level or higher.

Prepare and validate structured datasets through cleaning, transformation, encoding, scaling, and missing-value handling.
Conduct exploratory data analysis to identify patterns, data quality issues, and modeling opportunities.
Build, test, and compare models for regression, classification, clustering, dimensionality reduction, and anomaly detection.
Develop and select features with guidance from senior engineers.
Run experiments, tune model parameters, and evaluate results using suitable validation approaches and metrics.
Help maintain reproducible machine-learning pipelines, notebooks, experiment tracking, and technical documentation.
Write and maintain automated tests for data- and model-related code.
Support model delivery through REST APIs or batch-processing workflows.
Document model assumptions, results, limitations, and technical decisions.
Work with senior engineers, incorporate feedback, and gradually take ownership of more complex tasks.

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