Artificial Intelligence Engineer

Job Title:

Experience:

Location:

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).