Machine Learning Engineer

Role Overview

• Design, develop, and validate predictive models, regression algorithms, and time‑series forecasting models with a strong focus on performance, accuracy, and robustness.
• Contribute to the full model lifecycle: research, experimentation, industrialization, deployment, and monitoring.
• Build machine learning and deep learning models using Python, Spark MLlib, TensorFlow, and PyTorch.
• Optimize and integrate models into distributed data pipelines running on Cloudera, Spark, and Data-as-a-Service (DaaS) architectures.
• Collaborate with Data Engineers to ensure efficient data ingestion, preparation, and feature engineering in large‑scale environments.
• Work with Data Architects to ensure algorithmic solutions comply with architectural principles, data governance practices, and security standards.
• Partner with Data Scientists to design experiments, evaluate feature sets, and improve model quality.
• Contribute to product‑oriented initiatives by working with Product Managers and occasionally customers to ensure models address real business needs.
• Apply best practices in MLOps, including CI/CD for ML, model monitoring, drift detection, and automated retraining.
• Ensure strict compliance with data privacy, security, and governance policies across all algorithmic developments.
• Stay informed on the latest advances in machine learning, deep learning, and model optimization techniques.
• Participate in agile ceremonies such as sprint planning, architecture reviews, and continuous integration/deployment activities.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Physics, or a related field.
• Demonstrated experience in predictive modelling, regression, and time‑series analysis
• Machine learning and deep learning techniques
• Python and related scientific libraries (NumPy, Pandas, Scikit‑learn)
• TensorFlow or PyTorch
• Spark MLlib for distributed model training
• Deploying models into production environments
• Hands‑on experience or strong motivation to work with on-premise platforms and cloud platforms.
• Curiosity and the ability to rapidly learn new technologies, frameworks, and research methods.
• Strong analytical, problem‑solving, and communication skills.
• Ability to work collaboratively in multidisciplinary, international teams.
• English is a must — you must be able to communicate effectively with global stakeholders.

Tech Stack

• Cloud
• Numpy
• Pandas
• Python
• PyTorch
• Spark
• Tensorflow

Benefits

• Health insurance
• Professional development opportunities