ML Engineer

About the Role

We are looking for a Machine Learning Engineer with a strong foundation in classical ML techniques to design, develop, and deploy models that power decision-making and automation. This role focuses on structured/tabular data, time series, and predictive modeling using established libraries and approaches (e.g., scikit-learn, AutoML frameworks), rather than deep learning or LLMs.

What You’ll Do

• Design, build, and optimize supervised and unsupervised ML models (classification, regression, clustering, anomaly detection, recommendation, etc.).
• Work with tabular, structured, and time-series data across different business domains.
• Apply feature engineering, data preprocessing, and dimensionality reduction techniques to improve model quality.
• Experiment with AutoML frameworks (e.g., H2O, Auto-sklearn, TPOT) to streamline experimentation and benchmarking.
• Evaluate models with robust metrics (AUC, RMSE, precision/recall, F1, etc.) and ensure they generalize well.
• Collaborate with data engineers to design efficient data pipelines for training and inference.
• Deploy models into production environments (batch and real-time) and monitor performance drift.
• Document methodologies and communicate findings to both technical and non-technical stakeholders.

What We’re Looking For

• Strong programming skills in Python (scikit-learn, pandas, NumPy, matplotlib/seaborn for analysis/visualization).
• Solid understanding of statistics, probability, and optimization as applied to machine learning.
• Experience with model selection, cross-validation, and hyperparameter tuning.
• Familiarity with AutoML tools (e.g., H2O, Auto-sklearn, MLflow AutoML, or similar).
• Good knowledge of SQL and working with structured data sources.
• Experience deploying ML models (using Flask/FastAPI, MLflow, or cloud ML services).
• Strong problem-solving skills and ability to translate business problems into ML solutions.
• Bonus: exposure to time-series forecasting (ARIMA, Prophet, Gradient Boosting, etc.), ensemble methods (XGBoost, LightGBM, CatBoost).

Why Join Us?

You’ll work on impactful projects where classic ML delivers measurable value—from forecasting and optimization to anomaly detection and risk scoring. Unlike roles focused on generative AI, here you’ll focus on data-driven predictive modeling, experimentation, and building scalable, interpretable solutions.