Job description

AI Engineer We are seeking a skilled AI Engineer with 35 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).

Requirements