Description

You will design, build, and deploy scalable AI/ML models for banking applications, including fraud detection and credit scoring.

This role is on-site.

Responsibilities

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

Required Skills

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

Preferred Skills

• Experience in the financial or banking sector.
• Familiarity with advanced data governance frameworks.

Key Skills

Education

Bachelor's degree