Hybrid Machine Learning (AI) Engineer

About the role

We are seeking a Hybrid Machine Learning (AI) Engineer to design, develop, and deploy intelligent models that power core product features. The role works closely with cross‑functional teams to create scalable AI solutions that improve user experience and operational efficiency.

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.
• 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 Git version control and CI/CD pipelines.

Required skills

• Python
• TensorFlow
• PyTorch
• MLflow
• Kubeflow
• SageMaker
• AWS
• GCP
• Azure
• Git
• CI/CD

What we offer

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