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. The position works remotely with a hybrid collaboration model, partnering with data scientists, software engineers, and product teams to deliver scalable AI solutions.

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 (MLflow, Kubeflow, SageMaker) for streamlined deployment, monitoring, and version control.
• Collaborate with cross‑functional teams 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

• Proven experience developing and deploying machine learning models in a production environment.
• 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

What we offer

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