Hybrid Machine Learning (AI) Engineer

About the role

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

Key responsibilities

• Design, train, and optimize machine learning models using Python, TensorFlow, and PyTorch.
• Develop and maintain robust data pipelines to ensure high‑quality inputs for model training and evaluation.
• Implement MLOps practices, including 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

• Proven experience building and scaling machine learning models.
• Hands‑on expertise 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
• GCP
• Azure
• Git
• CI/CD pipelines
• Data pipelines
• MLOps

What we offer

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