Description

You will design and implement scalable AI/ML models for banking applications like fraud detection and credit scoring.

Responsibilities

• Deploy and manage ML models in production using Azure ML and AWS SageMaker.
• Collaborate with data science, software, and DevOps teams to operationalize ML workflows.
• Build and maintain CI/CD pipelines for ML model deployment and monitoring.
• Ensure model performance optimization and compliance with data governance standards.
• Document model processes and deployment strategies for knowledge sharing.

Required Skills

• 8+ years of professional experience.
• Strong Python programming skills, including Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch.
• Hands-on experience with Azure ML and AWS SageMaker.
• Familiarity with ML lifecycle tools like MLflow, Kubeflow, or Airflow.
• Experience with containerization (Docker) and orchestration (Kubernetes).
• Proficiency in Git and CI/CD tools (GitHub Actions, Azure DevOps).
• Experience deploying models as REST APIs or batch jobs.
• Bachelor's degree.

Key Skills

Education

Bachelor's degree