Machine Learning (AI) Engineer – Hybrid

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 development leader. The position is hybrid, allowing collaboration with cross‑functional teams while working remotely part of the time.

Key responsibilities

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

Required profile

• Strong background in data preprocessing, feature engineering, and model evaluation metrics.
• Experience with cloud platforms (AWS, GCP, or Azure) for scalable AI infrastructure.
• Familiarity with software‑engineering best practices, including Git version control and CI/CD pipelines.

Required skills

• Python
• TensorFlow
• PyTorch
• MLflow
• Kubeflow
• SageMaker
• AWS
• Google Cloud Platform
• Microsoft Azure
• Git
• CI/CD pipelines
• MLOps

What we offer

• Opportunity to work with a global leader in software development.
• Access to cutting‑edge tools, large datasets, and collaborative environment.
• Chance to solve complex technical challenges in AI‑driven product development.