ML Engineer
Core
Prepare and validate structured datasets, build, test, and compare ML models (regression, classification, clustering, anomaly detection), and support model delivery via REST APIs or batch workflows.
Role type
Junior ML Engineer (structured learning & practical delivery)
Builds
Production ML models and data pipelines
Domain
Data Science / Machine Learning
Deliverable
production ML models
Required skills
Python, pandas, NumPy, scikit-learn, SQL, XGBoost, LightGBM, feature engineering, hyperparameter tuning, model evaluation, REST API integration, Docker, Git, automated testing
Preferred skills
MLflow, time-series analysis, recommendation systems, model interpretability, cloud services, GenAI, LLM, RAG, prompt engineering
Technologies
Python, pandas, NumPy, scikit-learn, Jupyter, SQL, XGBoost, LightGBM, MLflow, FastAPI, Git, Docker, pytest
Responsibilities
Prepare and validate structured datasets (cleaning, transformation, encoding, scaling, missing-value handling); Conduct exploratory data analysis to identify patterns and data quality issues; Build, test, and compare ML 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; Help maintain reproducible ML pipelines, notebooks, 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.
Seniority
Junior, hands-on IC