ML Engineer

## Project Description You will work with senior engineers on internal classical machine learning initiatives. The role combines structured learning, practical delivery, and gradual ownership of increasingly complex tasks. ## Technologies - Python, pandas, NumPy, scikit-learn, Jupyter - SQL - XGBoost, LightGBM - MLflow - FastAPI - Git, Docker, pytest ## What You'll Do - Prepare and validate structured datasets for machine learning tasks, including cleaning, transformation, encoding, scaling, and missing-value handling. - Conduct exploratory data analysis to identify patterns, data quality issues, and opportunities for modeling. - Build, test, and compare ML models for tasks such as 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 appropriate validation approaches and metrics. - Help maintain reproducible ML pipelines, notebooks, experiment tracking, and technical documentation. - Write and maintain automated tests for data and model-related code. - Support the delivery of models through REST APIs or batch-processing workflows. - Document model assumptions, results, limitations, and technical decisions. - Work closely with senior engineers, incorporate feedback, and gradually take ownership of more complex tasks. ## Job Requirements - 1+ year of hands-on Python development experience, including experience with pandas, NumPy, scikit-learn, and Jupyter. - A strong foundation in statistics, probability, and linear algebra, plus exploratory data analysis skills. - An understanding of supervised and unsupervised learning, and practical experience with regression, classification, clustering, dimensionality reduction, and anomaly detection. - Experience preparing structured datasets, including cleaning, transformation, encoding, scaling, and missing-value handling, plus practical feature engineering and feature-selection skills. - An understanding of train/validation/test splits, cross-validation, data leakage, overfitting, and regularization; the ability to select appropriate evaluation metrics, compare models, and perform error analysis. - Familiarity with hyperparameter tuning and reproducible ML pipelines. - SQL skills for data extraction and analysis. - The ability to expose models through REST APIs or batch-processing workflows. - Familiarity with Git, automated testing, Docker, and basic model monitoring. - The ability to explain model behavior, assumptions, limitations, and results. - English at B2 level or higher. ## Nice To Have - XGBoost, time-series analysis, recommendation systems, MLflow, model interpretability, cloud services, production ML monitoring, and GenAI, LLM, RAG, prompt-engineering, or agent-development experience. ## What Do We Offer The global benefits package includes: - Technical and non-technical training for professional and personal growth. - Internal conferences and meetups to learn from industry experts. - Support and mentorship from an experienced employee to help you grow and develop professionally. - Health insurance. - Sports activities to promote a healthy lifestyle. - Flexible work options, including remote and hybrid opportunities. - Referral program for bringing in new talent. - Work anniversary program and additional vacation days.