Role Overview: We are seeking a skilled AI Engineer with 3–5 years of hands-on experience in designing, developing, and deploying AI/ML solutions in cloud environments. The ideal candidate will have strong proficiency in Python, experience with both Azure and AWS, and a solid understanding of MLOps practices. Key Responsibilities:

• Design and implement scalable AI/ML models for banking applications (e.g., fraud detection, credit scoring, customer segmentation).

• Deploy and manage models in production using Azure ML and AWS SageMaker.

• Collaborate with data scientists, software engineers, and DevOps teams to operationalize ML workflows.

• Build and maintain CI/CD pipelines for ML model deployment and monitoring.

• Ensure compliance with data governance, security, and regulatory standards.

• Optimize model performance and resource usage in cloud environments.

• Document processes, models, and deployment strategies for internal knowledge sharing.

Required Skills:

• Programming: Strong Python skills, including libraries like Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch.

• Cloud Platforms: Hands-on experience with Azure ML, AWS SageMaker, and cloud-native services (e.g., Lambda, EC2, S3, Azure Functions).

• MLOps: Familiarity with ML lifecycle tools (MLflow, Kubeflow, Airflow), containerization (Docker), and orchestration (Kubernetes).

• Deployment: Experience deploying models as REST APIs or batch jobs in production environments.

• Version Control & CI/CD: Git, GitHub Actions, Azure DevOps, or AWS CodePipeline.

• Monitoring & Logging: Tools like Prometheus, Grafana, or cloud-native monitoring solutions.

• Preferred Qualifications:

• Experience in the banking or financial services domain.

• Knowledge of data privacy regulations (e.g., GDPR, PSD2).

• Exposure to generative AI or LLM fine-tuning is a plus. (Certifications in Azure or AWS) (e.g., Azure AI Engineer Associate, AWS Certified Machine Learning).