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 leader. The position works closely with data scientists, software engineers, and product teams to deliver scalable AI solutions that improve user experience and operational efficiency.

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—including model versioning, deployment, monitoring, and CI/CD—to streamline production workflows.
• Collaborate with cross‑functional teams to integrate AI capabilities into existing systems.
• Conduct performance benchmarking, A/B testing, and impact analysis to validate model accuracy and business value.

Required profile

• Proven experience developing ML models with Python, TensorFlow, and PyTorch.
• Hands‑on experience with MLOps tools such as MLflow, Kubeflow, or SageMaker.
• Strong background in data preprocessing, feature engineering, and model evaluation metrics.
• Familiarity with cloud platforms (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
• Google Cloud Platform (GCP)
• Microsoft Azure
• Git
• CI/CD pipelines