AI Engineers

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.

• 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 Sage Maker.
• Collaborate with data scientists, software engineers, and Dev Ops 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.

• Programming:
  Strong Python skills, including libraries like Pandas, Num Py, Scikit‑learn, Tensor Flow or PyTorch.
• Cloud Platforms:
  Hands‑on experience with Azure ML, AWS Sage Maker, 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, Git Hub Actions, Azure Dev Ops, or AWS Code Pipeline.
• 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).