AI Machine Learning Engineer

About the Role

Global Aerospace Logistics (GAL) is actively looking for a skilled AI Machine Learning Engineer to be part of its AI and Digital Transformation team. The selected candidate will be responsible for designing, developing, deploying, and sustaining machine learning solutions aimed at enhancing operational efficiency, enabling automation, and facilitating data-driven decision-making throughout the organization.

This role involves collaborating closely with data scientists, software engineers, and business stakeholders to construct scalable AI applications and transition machine learning models into production environments.

Responsibilities

• Design, develop, train, and deploy machine learning models effectively.
• Create scalable data processing pipelines and manage feature engineering workflows.
• Bring AI and machine learning models into production use.
• Deploy models using APIs, microservices architecture, and cloud platforms.
• Continuously monitor, retrain, and enhance model performance to maintain effectiveness.
• Implement MLOps practices including continuous integration, continuous delivery, model versioning, and monitoring.
• Maintain data quality, ensure security, and comply with regulatory requirements.
• Prepare comprehensive documentation for AI systems, models, and workflows.
• Collaborate with multidisciplinary teams to successfully implement AI initiatives.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related discipline.
• Proficient programming skills in Python.
• Exposure to Java, C++, or JavaScript is beneficial.

Experience Requirements

• Between 3 to 8 years of professional experience in Machine Learning, Data Science, or Software Engineering.
• At least 2 years of practical experience in deploying machine learning models in production environments.
• Hands-on experience with model development, feature engineering, data pipelines, and deployment, familiar with MLOps tools such as MLflow, Kubeflow, or Airflow.
• Experience or knowledge in Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), Computer Vision, or related AI technologies is a plus.