Hybrid Machine Learning (AI) Engineer

About the role

We are looking for a Machine Learning (AI) Engineer to design, develop, and deploy AI models that power core product features for a global software leader. The position is hybrid, combining remote work with occasional on‑site collaboration, and offers the chance to work with cutting‑edge tools and large datasets.

Key responsibilities

• Design, train, and optimize machine learning models using Python, TensorFlow, and PyTorch.
• Develop and maintain data pipelines to ensure high‑quality input for model training and evaluation.
• Implement MLOps practices to streamline model deployment, monitoring, and version control.
• 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

• Proficiency in Python, TensorFlow, and PyTorch for model development and training.
• 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.
• Knowledge of software engineering best practices, including version control (Git) and CI/CD pipelines.

Required skills

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

What we offer

• Opportunity to work with a global leader in software development.
• Access to cutting‑edge AI tools and large datasets.
• Collaborative, innovative environment.
• Competitive compensation package.