Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI): Developing innovative solutions in prediction, regression, time-series forecasting, and sustainability-driven analytics. Focus on building machine learning and deep learning models using Python, Spark MLlib, TensorFlow, and PyTorch and ensuring strict compliance with data privacy, security, and governance policies across all algorithmic developments.

Location: Madrid, Seville

Company

hirify.global, LLC. is seeking a Machine Learning Engineer to join their international team as part of a strategic expansion of AI capabilities.

What you will do

• Design, develop, and validate predictive models, regression algorithms, and time‑series forecasting models.
• Contribute to the full model lifecycle: research, experimentation, industrialization, deployment, and monitoring.
• Optimize and integrate models into distributed data pipelines running on Cloudera, Spark, and Data-as-a-Service (DaaS) architectures.
• Collaborate with Data Engineers and Data Architects to ensure efficient data ingestion, preparation, and feature engineering in large‑scale environments.
• Partner with Data Scientists to design experiments, evaluate feature sets, and improve model quality.
• Apply best practices in MLOps, including CI/CD for ML, model monitoring, drift detection, and automated retraining.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Physics, or a related field.
• Demonstrated experience in predictive modeling, regression, and time‑series analysis, machine learning and deep learning techniques.
• Experience with Python and related scientific libraries (NumPy, Pandas, Scikit‑learn), TensorFlow or PyTorch, Spark MLlib for distributed model training.
• Experience deploying models into production environments.
• Ability to work collaboratively in multidisciplinary, international teams.
• English is a must — you must be able to communicate effectively with global stakeholders.

Culture & Benefits

• Agile ceremonies such as sprint planning, architecture reviews, and continuous integration/deployment activities.
• Opportunity to stay informed on the latest advances in machine learning, deep learning, and model optimization techniques.
• Work in multidisciplinary, international teams.