AI Engineers
Role Overview: 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).
AI Engineers
Role Overview: 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).