Machine Learning (AI) Engineer – Hybrid (Remote)

About the role

We are seeking a Machine Learning (AI) Engineer to design, develop, and deploy intelligent models that power core product features for a global software leader. The position is hybrid, allowing you to work remotely while collaborating with cross‑functional teams to deliver scalable AI solutions.

Key responsibilities

• Design, train, and optimize machine‑learning models using Python, TensorFlow, and PyTorch.
• Build and maintain robust data pipelines to ensure high‑quality input for model training and evaluation.
• Implement MLOps practices with tools such as MLflow, Kubeflow, or SageMaker to streamline deployment, monitoring, and version control.
• Collaborate with data scientists and software engineers to integrate AI capabilities into production systems.
• Conduct performance benchmarking, A/B testing, and impact analysis to validate model accuracy and business value.

Required profile

• Strong proficiency in Python and deep‑learning frameworks (TensorFlow, PyTorch).
• Hands‑on experience with MLOps platforms (MLflow, Kubeflow, SageMaker) for model lifecycle management.
• Solid background in data preprocessing, feature engineering, and evaluation metrics.
• Familiarity with cloud environments (AWS, GCP, or Azure) for scalable AI infrastructure.
• Understanding of software engineering best practices, including Git version control and CI/CD pipelines.

Required skills

• Python
• TensorFlow
• PyTorch
• MLflow
• Kubeflow
• SageMaker
• AWS
• GCP
• Azure
• Git
• CI/CD

What we offer

• Opportunity to work with a global leader in software development.
• Access to cutting‑edge tools, large datasets, and a collaborative environment.
• Competitive compensation ranging from $200K to $500K per year.